Held in conjunction with KDD'20
Aug 24, 2020 - Virtual
16th International Workshop on
Mining and Learning with Graphs
Schedule

Introduction

There is a great deal of interest in analyzing data that is best represented as a graph. Examples include the WWW, social networks, biological networks, communication networks, transportation networks, energy grids, and many others. These graphs are typically multi-modal, multi-relational and dynamic. In the era of big data, the importance of being able to effectively mine and learn from such data is growing, as more and more structured and semi-structured data is becoming available. The workshop serves as a forum for researchers from a variety of fields working on mining and learning from graphs to share and discuss their latest findings.
There are many challenges involved in effectively mining and learning from this kind of data, including:

  • Understanding the different techniques applicable, including graph mining algorithms, network embeddings, graphical models, latent variable models, matrix factorization methods and more.
  • Dealing with the heterogeneity of the data.
  • The common need for information integration and alignment.
  • Handling dynamic and changing data.
  • Addressing each of these issues at scale.

Traditionally, a number of subareas have contributed to this space: communities in graph mining, learning from structured data, statistical relational learning, inductive logic programming, and, moving beyond subdisciplines in computer science, social network analysis, and, more broadly network science.

Schedule

Virtual (All times are Pacific Time)

All accepted papers will be presented in the poster session.
Based on scheduling constraints, some papers are selected as contributed talks or spotlight presentations.
-Held jointly with International Workshop on Deep Learning on Graphs (KDD-DLG)-

Morning Sessions
8:00 am Opening Remarks
8:15 am
Keynote:
Jure Leskovec
Graph Structure of Neural Networks: Good Neural Networks Are Alike
8:45 am
Keynote:
Philip Yu
Broad Learning Via Heterogenous Information Networks
9:15 am Parallel Contributed Talks

Junchen Jin
Understanding and Evaluating Structural Node Embeddings

Caleb Belth
Mining Persistent Activity in Continually Evolving Networks
9:45 am
Keynote:
Fei Wang
Deep Graph Mining for Healthcare
10:15 am Coffee Break/Social Networking
10:30 am
Keynote:
Danai Koutra
The Power of Summarization in Network Analysis
Video
11:00 am
Keynote:
Petar Veličković
Algorithmic Inductive Biases
11:30 am Parallel Poster Session (Spotlight Talks + Live Q/A)
Breakout Z-rooms for both DLG and MLG
12:00 pm Lunch Break
Afternoon Sessions
1:00 pm
Keynote:
Jimeng Sun
Deep Learning for Drug Development
1:30 pm Parallel Contributed Talks

William Shiao
BRGAN: Generating Graphs of Bounded Rank

Christopher Tran
Heterogeneous Threshold Estimation for Linear Threshold Modeling
2:00 pm
Keynote:
Tyler Derr
Self-supervised Learning on Graphs: Deep Insights and New Directions
2:30 pm Coffee Break/Social Networking
2:45 pm
Keynote:
Muhan Zhang
Learning Graph Strcuture Features for Inductive Link Prediction and Matrix Completion
3:15 pm
Keynote:
Le Song
Cybersecurity with Graph Neural Networks
3:45 pm Best Paper Award Ceremony + Closing Remarks
4:00 pm Parallel Poster Session (Spotlight Talks + Live Q/A)
Breakout Z-rooms for both DLG and MLG
 

Keynote Speakers

Jointly with the International Workshop on Deep Learning on Graphs (KDD-DLG)

 
Danai Koutra

Danai Koutra

University of Michigan Ann Arbor

Jure Leskovec

Jure Leskovec

Stanford University

Fei Wang

Fei Wang

Cornell University

Philip Yu

Philip Yu

University of Illinois at Chicago

Le Song

Le Song

Georgia Institute of Technology

 
 
Jimeng Sun

Jimeng Sun

University of Illinois Urbana-Champaign

Petar Velickovic

Petar Velickovic

Deepmind

Muhan Zhang

Muhan Zhang

Facebook AI

Tyler Derr

Tyler Derr

Vanderbilt University

 

Accepted Papers

 

A Scalable Parallel Hypergraph Generator (HyGen) PDF Video
S.M.Shamimul Hasan, Neena Imam and Ramakrishnan Kannan

Abstract: Graphs are extensively used to model real-world complex systems. An edge in a graph can model pairwise relationships. However, multiway relationships (connections between three or more vertices) are common in many complex systems such as cellular process, image segmentation, and circuit design. A graph edge cannot model multiway relationships. A hypergraph, which can connect more than two vertices, is thus a better option to model multiway relationships. A large-scale hypergraph analysis has the potential to find useful insights from a complex system and assist in knowledge discovery. Currently a limited number of hypergraphs exists that are representative of real-world datasets. Moreover, real-world hypergraph datasets are small in size and inadequate to incorporate future needs. A graph generator that can produce large-scale synthetic hypergraphs can solve the above mentioned problems. In this paper, we present a scalable parallel hypergraph generator (HyGen) based on the Message Passing Interface (MPI) standard. To generate hypergraphs, HyGen takes the following parameter values as inputs: i) number of vertices, ii) number of hyperedges, iii) number of clusters, iv) vertex distribution, v) hyperedge distribution, vi) local cluster cardinality, and vii) global cluster cardinality. We have demonstrated that HyGen can generate hypergraphs of various sizes in a scalable fashion. HyGen takes approximately four minutes to generate a hypergraph with 4.8 million vertices, 1.6 million hyperedges, and 800 clusters using 1,024 processes on a leadership class computing platform. Our strong and weak scaling experiments on supercomputers demonstrate that HyGen can quickly create large-scale hypergraphs in a parallel manner, thus providing a useful capability for hypergraph analysis.

Keywords: Hypergraph, MPI, C++, OLCF, Rhea
@inproceedings{mlg2020_5,
title={A Scalable Parallel Hypergraph Generator (HyGen)},
author={S.M.Shamimul Hasan, Neena Imam and Ramakrishnan Kannan},
booktitle={Proceedings of the 16th International Workshop on Mining and Learning with Graphs (MLG)},
year={2020}
}

Active Learning on Graphs with Geodesically Convex Classes PDF Video
Maximilian Thiessen and Thomas Gärtner

Abstract: We study the problem of actively learning the vertex labels of a graph, assuming the classes form geodesically convex subgraphs, which is related to linear separability in the Euclidean setting. The main result of this paper is a novel query-efficient active learning algorithm with label-independent upper bounds on the number of queries needed to learn all labels. For that, we use shortest path covers and provide a logarithmic approximation for the subproblem of computing a shortest path cover of minimum size. We extend the approach to arbitrarily labeled graphs using a convexity-based selection criterion. Finally, we discuss whether the convexity assumption holds on real-world data and give some first preliminary results on citation and image benchmark datasets.

Keywords: active learning, graphs, geodesic convexity, node classification, multi-class, path cover
@inproceedings{mlg2020_40,
title={Active Learning on Graphs with Geodesically Convex Classes},
author={Maximilian Thiessen and Thomas Gärtner},
booktitle={Proceedings of the 16th International Workshop on Mining and Learning with Graphs (MLG)},
year={2020}
}

Adaptive Granularity in Time Evolving Graphs as Tensors PDF Video
Ravdeep Pasricha, Ekta Gujral and Evangelos Papalexakis

Abstract: Data collected at very frequent intervals is usually extremely sparse and has no structure that is exploitable by modern tensor decomposition algorithms. Thus the utility of such tensors is low, in terms of the amount of interpretable and exploitable structure that one can extract from them. In this paper, we introduce the problem of finding a tensor of adaptive aggregated granularity that can be decomposed to reveal meaningful latent concepts (structures) from datasets that, in their original form, are not amenable to tensor analysis. Such datasets fall under the broad category of sparse point processes that evolve over space and/or time. To the best of our knowledge, this is the first work that explores adaptive granularity aggregation in tensors. Furthermore, we formally define the problem and discuss what different definitions of “good structure” can be in practice, and show that optimal solution is of prohibitive combinatorial complexity. Subsequently, we propose an efficient and effective greedy algorithm which follows a number of intuitive decision criteria that locally maximize the “goodness of structure”, resulting in high-quality tensors. We evaluate our method on synthetic and semi-synthetic data where ground truth is known. Our proposed method constructs tensors that have very high structure quality.

Keywords: Tensor analysis, unsupervised learning, time-evolving graphs
@inproceedings{mlg2020_35,
title={Adaptive Granularity in Time Evolving Graphs as Tensors},
author={Ravdeep Pasricha, Ekta Gujral and Evangelos Papalexakis},
booktitle={Proceedings of the 16th International Workshop on Mining and Learning with Graphs (MLG)},
year={2020}
}

Adversarial Learning for Debiasing Knowledge Graph Embeddings PDF Video
Mario Arduini, Lorenzo Noci, Federico Pirovano, Ce Zhang, Yash Raj Shrestha and Bibek Paudel

Abstract: Knowledge Graphs (KG) are gaining increasing attention in both academia and industry. Despite their diverse benefits, recent research have identified social and cultural biases embedded in the representations learned from KGs. Such biases can have detrimental consequences on different population and minority groups as applications of KG begin to intersect and interact with social spheres. This paper describes our work-in-progress, which aims at identifying and mitigating such biases in Knowledge Graph (KG) embeddings. As a first step, we explore popularity bias --- the relationship between node popularity and link prediction accuracy. In case of node2vec graph embeddings, we find that prediction accuracy of the embedding is negatively correlated with the degree of the node. However, in case of knowledge-graph embeddings (KGE), we observe an opposite trend. As a second step, we explore gender bias in KGE, and a careful examination of popular KGE algorithms suggest that sensitive attribute like the gender of a person can be predicted from the embedding. This implies that such biases in popular KGs is captured by the structural properties of the embedding. As a preliminary solution to debiasing KGs, we introduce a novel framework to filter out the sensitive attribute information from the KG embeddings, which we call FAN (Filtering Adversarial Network). We also suggest the applicability of FAN for debiasing other network embeddings which could be explored in future work.

Keywords: knowledge graph embedding, network embedding, adversarial network, bias in machine learning
@inproceedings{mlg2020_39,
title={Adversarial Learning for Debiasing Knowledge Graph Embeddings},
author={Mario Arduini, Lorenzo Noci, Federico Pirovano, Ce Zhang, Yash Raj Shrestha and Bibek Paudel},
booktitle={Proceedings of the 16th International Workshop on Mining and Learning with Graphs (MLG)},
year={2020}
}

BRGAN: Generating Graphs of Bounded Rank PDF Video
William Shiao and Evangelos Papalexakis

Abstract: Graph generation is a task that has been explored with a wide variety of methods. Recently, several papers have applied Generative Adversarial Networks (GANs) to this task, but most of these methods result in graphs of full or unknown rank. Many real-world graphs have low rank, which roughly translates to the number of communities in that graph. Furthermore, it has been shown that taking the low rank approximation of a graph can defend against adversarial attacks. This suggests that testing models against graphs of different rank may be useful. However, current methods provide no way to control the rank of generated graphs. In this paper, we propose BRGAN: a GAN architecture that generates synthetic graphs, which in addition to having realistic graph features, also have bounded (low) rank. We extensively evaluate BRGAN and show that it is able to generate synthetic graphs competitive with state-of-the-art models, with rank equal to or lower than the desired rank.

Keywords: graph generation, generative adversarial networks, graph rank
@inproceedings{mlg2020_3,
title={BRGAN: Generating Graphs of Bounded Rank},
author={William Shiao and Evangelos Papalexakis},
booktitle={Proceedings of the 16th International Workshop on Mining and Learning with Graphs (MLG)},
year={2020}
}

CONE-Align: Consistent Network Alignment with Proximity-Preserving Node Embedding PDF Video
Xiyuan Chen, Mark Heimann, Fatemeh Vahedian and Danai Koutra

Abstract: Network alignment, the process of finding correspondences between nodes in different graphs, has many scientific and industrial applications. Existing unsupervised network alignment methods find suboptimal alignments that break up node neighborhoods, i.e. do not preserve matched neighborhood consistency. To improve this, we propose CONE-Align, which models intra-network proximity with node embeddings and matches nodes across networks by comparing the embeddings after aligning their subspaces. Experiments on diverse, challenging datasets show that CONE-Align is robust and obtains up to 49% greater accuracy than the state-of-the-art graph alignment algorithms.

Keywords: network alignment, graph mining, node embeddings, neighborhood consistency
@inproceedings{mlg2020_4,
title={CONE-Align: Consistent Network Alignment with Proximity-Preserving Node Embedding},
author={Xiyuan Chen, Mark Heimann, Fatemeh Vahedian and Danai Koutra},
booktitle={Proceedings of the 16th International Workshop on Mining and Learning with Graphs (MLG)},
year={2020}
}

Characterising the atomic structure of mono-metallic nanoparticles from x-ray scattering data using conditional generative models PDF Video
Andy S. Anker, Emil T. S. Kjær, Erik B. Dam, Simon J. L. Billinge, Kirsten M. Ø. Jensen and Raghavendra Selvan

Abstract: The development of new nanomaterials for energy technologies is dependent on understanding the intricate relation between material properties and atomic structure. It is, therefore, crucial to be able to routinely characterise the atomic structure in nanomaterials, and a promising method for this task is Pair Distribution Function (PDF) analysis. The PDF can be obtained through Fourier transformation of x-ray total scattering data, and represents a histogram of all interatomic distances in the sample. Going from the distance information in the PDF to a chemical structure is an unassigned distance geometry problem (uDGP), and solving this is often the bottleneck in nanostructure analysis. In this work, we propose to use a Conditional Variational Autoencoder (CVAE) to automatically solve the uDGP to obtain valid chemical structures from PDFs. We use a simple model system of hypothetical mono-metallic nanoparticles containing up to 100 atoms in the face centered cubic (FCC) structure as a proof of concept. The model is trained to predict the assigned distance matrix (aDM) from a simulated PDF of the structure as the conditional input. We introduce a novel representation of structures by projecting them inside a unit sphere and adding additional anchor points or satellites to help in the reconstruction of the chemical structure. The performance of the CVAE model is compared to a Deterministic Autoencoder (DAE) showing that both models are able to solve the uDGP reasonably well. We further show that the CVAE learns a structured and meaningful latent embedding space which can be used to predict new chemical structures.

Keywords: generative modeling, mono-metallic nanoparticles, CVAE, Pair Distribution Function
@inproceedings{mlg2020_22,
title={Characterising the atomic structure of mono-metallic nanoparticles from x-ray scattering data using conditional generative models},
author={Andy S. Anker, Emil T. S. Kjær, Erik B. Dam, Simon J. L. Billinge, Kirsten M. Ø. Jensen and Raghavendra Selvan},
booktitle={Proceedings of the 16th International Workshop on Mining and Learning with Graphs (MLG)},
year={2020}
}

Collective Bio-Entity Recognition in Scientific Documents using Hinge-Loss Markov Random Fields PDF Video
Alexander Miller, Naum Markenzon, Varun Embar and Lise Getoor

Abstract: Identifying biological entities such as genes and proteins from scientific documents is crucial for further downstream tasks such as question answering and information retrieval. This task is challenging because the same surface text can refer either to a gene or a protein based on the context. Traditional approaches such as Huang et al. consider the words present in the surrounding text to infer the context. However, they fail to consider the semantics of these words which are better represented by contextual word embeddings such as BERT. Deep learning based approaches, on the other hand, fail to make use of the relational structure of scientific documents. We introduce a novel probabilistic approach that jointly classifies all entity references using a class of undirected graphical models called hinge-loss Markov random fields. Our approach can combine relational information with embedding-based word semantics. Further, our approach can be easily extended to incorporate new sources of information. Our initial evaluation on the JNLPBA shared task corpus shows that our joint classification approach outperforms both traditional machine learning approaches and semantic models based on word embeddings by up to 7.5% on F1 score.

Keywords: probabilistic graphical models, embeddings, information extraction, word sense disambiguation, graphs, collective inference
@inproceedings{mlg2020_28,
title={Collective Bio-Entity Recognition in Scientific Documents using Hinge-Loss Markov Random Fields},
author={Alexander Miller, Naum Markenzon, Varun Embar and Lise Getoor},
booktitle={Proceedings of the 16th International Workshop on Mining and Learning with Graphs (MLG)},
year={2020}
}

Comparison of Graph Generation Models focusing on Accuracy and Variation PDF Video
Mei Fukuda, Kazuki Nakajima and Kazuyuki Shudo

Abstract: Generation models of graphs have been used to compare and analyze the properties of graph structures and to produce graphs that resemble real-world networks. When using a generation model to mimic a real-world network, it is desirable for the error in the properties between the target graph and the generated graph and the variation of the errors between generated graphs are small. However, since many existing generation models generate graphs by adding edges at random, the extent of the error and its variation for each generated graph is unclear. This paper studies the error and the variation of properties of graphs generated using the dK-series framework, which has been proposed to analyze the topology of a network based on the degree of nodes. In addition, we propose a new graph generation model that takes the degree distribution and degree-dependent clustering coefficient as inputs. We show that the proposed model is able to reduce the error to a greater extent than other generation models.

Keywords: network analysis, graph generation models, estimation, sampling, social networks
@inproceedings{mlg2020_2,
title={Comparison of Graph Generation Models focusing on Accuracy and Variation},
author={Mei Fukuda, Kazuki Nakajima and Kazuyuki Shudo},
booktitle={Proceedings of the 16th International Workshop on Mining and Learning with Graphs (MLG)},
year={2020}
}

Decoupled Smoothing in Probabilistic Soft Logic PDF Video
Yatong Chen, Bryan Tor, Eriq Augustine and Lise Getoor

Abstract: Node classification in networks is a common graph mining task. In this paper, we examine how separating identity(a node’s attribute)and preference(the kind of identities to which a node prefers to link)is useful for node classification in social networks. Building upon recent work by Chin et al., where the separation of identity and preference is accomplished through a technique called “decoupled smoothing”, we show how models that characterize both identity and preference are able to capture the underlying structure in a network, which leads to performance improvement in node classification tasks. Specifically, we use probabilistic soft logic (PSL), a flexible and declarative statistical reasoning framework, to model identity and preference. We compare our method with the original decoupled smoothing method and other node classification methods implemented in PSL, and show that our approach outperforms the state-of-the-art decoupled smoothing method as well as the other node classification methods across all evaluation metrics on a real-world Facebook Dataset.

Keywords: Node Classification, Social Network Analysis, Decoupled Smoothing, Statistical Relational Learning
@inproceedings{mlg2020_31,
title={Decoupled Smoothing in Probabilistic Soft Logic},
author={Yatong Chen, Bryan Tor, Eriq Augustine and Lise Getoor},
booktitle={Proceedings of the 16th International Workshop on Mining and Learning with Graphs (MLG)},
year={2020}
}

Effectiveness of Sampling Strategies for One-shot Active Learning from Relational Data PDF Video
Ragib Ahsan and Elena Zheleva

Abstract: Relational classification exploits structural information in network data to improve predictive performance. However, the large size of real-world networks causes two main scalability issues for relational classification. First, training supervised models on large networks is computationally expensive. Second, label acquisition for large samples can be costly and unrealistic. The goal of Active learning is to query informative labels and reduce labeling cost. However, state-of-the-art active learning strategies require multiple iterations of learning, in order to pick the best labels at each iteration, which incurs higher computational cost. In this work, we focus on a constrained version of the problem, named one-shot active learning where the active learner has to decide which nodes to sample in one shot, rather than iteratively. We consider several simple and network-based sampling strategies as potential solutions to this problem. In our experiments, we show a comprehensive evaluation of eleven different sampling methods on four real world network datasets using four relational classifiers (wvRN, ICA, SGC, GraphSage), offering the first comparison between collective classification and neural network approaches for one-shot active learning. Moreover, we propose a novel sampling method based on Weisfeiler-Lehman graph labeling algorithm which shows overall best performance across all classifiers and datasets. We empirically show that some of the computationally cheaper one-shot active learning approaches can achieve comparable Micro-F1 scores to existing active learning methods that require multiple iterations.

Keywords: Graph sampling, Active learning, Relational classification
@inproceedings{mlg2020_30,
title={Effectiveness of Sampling Strategies for One-shot Active Learning from Relational Data},
author={Ragib Ahsan and Elena Zheleva},
booktitle={Proceedings of the 16th International Workshop on Mining and Learning with Graphs (MLG)},
year={2020}
}

Efficient Algorithms to Mine Maximal Span-Trusses From Temporal Graphs PDF Video
Quintino Francesco Lotito and Alberto Montresor

Abstract: Over the last decade, there has been an increasing interest in temporal graphs, pushed by a growing availability of temporally-annotated network data coming from social, biological and financial networks. Despite the importance of analyzing complex temporal networks, there is a huge gap between the set of definitions, algorithms and tools available to study large static graphs and the ones available for temporal graphs. An important task in temporal graph analysis is mining dense structures, i.e., identifying high-density subgraphs together with the span in which this high density is observed. In this paper, we introduce the concept of (k, \Delta)-truss (span-truss) in temporal graphs, a temporal generalization of the k-truss, in which k captures the information about the density and \Delta captures the time span in which this density holds. We then propose novel and efficient algorithms to identify maximal span-trusses, namely the ones not dominated by any other span-truss neither in the order k nor in the interval \Delta, and evaluate them on a number of public available datasets.

Keywords: temporal graphs, graph mining, dense structures, community detection, social networks analysis
@inproceedings{mlg2020_11,
title={Efficient Algorithms to Mine Maximal Span-Trusses From Temporal Graphs},
author={Quintino Francesco Lotito and Alberto Montresor},
booktitle={Proceedings of the 16th International Workshop on Mining and Learning with Graphs (MLG)},
year={2020}
}

Examining COVID-19 Forecasting using Spatio-Temporal GNNs PDF Video
Amol Kapoor, Xue Ben, Luyang Liu, Bryan Perozzi, Matt Barnes, Martin Blais and Shawn O'Banion

Abstract: In this work, we examine a novel forecasting approach for COVID-19 that uses Graph Neural Networks and mobility data. In contrast to existing time series forecasting models, the proposed approach learns from a single large-scale spatio-temporal graph, where nodes represent the region-level human mobility, spatial edges represent the human mobility based inter-region connectivity, and temporal edges represent node features through time. We evaluate initial experiments on the US county level COVID-19 dataset, and show that the rich spatial and temporal information unified by the graph neural network allows the model to learn complex dynamics and make more accurate forecasts compared to autoregressive statistical learning and sequence deep learning approaches. We believe this novel source of information combined with graph based deep learning approaches can be a powerful tool to understand the spread and evolution of COVID-19. We encourage others to further develop a novel modeling paradigm for infectious disease based on this high resolution mobility data.

Keywords: machine learning, graph learning, covid-19
@inproceedings{mlg2020_26,
title={Examining COVID-19 Forecasting using Spatio-Temporal GNNs},
author={Amol Kapoor, Xue Ben, Luyang Liu, Bryan Perozzi, Matt Barnes, Martin Blais and Shawn O'Banion},
booktitle={Proceedings of the 16th International Workshop on Mining and Learning with Graphs (MLG)},
year={2020}
}

First and Higher-Order Bipartite Embeddings PDF Video
Justin Sybrandt and Ilya Safro

Abstract: Typical graph embeddings may not capture type-specific bipartite graph features that arise in such areas as recommender systems, data visualization, and drug discovery. Machine learning methods utilized in these applications would be better served with specialized embedding techniques. We propose two embeddings for bipartite graphs that decompose edges into sets of indirect relationships between node neighborhoods. When sampling higher-order relationships, we reinforce similarities through algebraic distance on graphs. We also introduce ensemble embeddings to combine both into a "best of both worlds" embedding. The proposed methods are evaluated on link prediction and recommendation tasks and compared with other state-of-the-art embeddings. Our embeddings are found to perform better on recommendation tasks and equally competitive in link prediction. Although all considered embeddings are beneficial in particular applications, we demonstrate that none of those considered is clearly superior (in contrast to what is claimed in many papers). Therefore, we discuss the trade offs among them, noting that the methods proposed here are robust for applications relying on same-typed comparisons. Reproducibility: Our code, data sets, and results are all publicly available online at: sybrandt.com/2020/fobe_hobe.

Keywords: bipartite graphs, hypergraphs, graph embedding, algebraic distance on graphs, recommendation, link prediction
@inproceedings{mlg2020_15,
title={First and Higher-Order Bipartite Embeddings},
author={Justin Sybrandt and Ilya Safro},
booktitle={Proceedings of the 16th International Workshop on Mining and Learning with Graphs (MLG)},
year={2020}
}

GATCheck: A Detailed Analysis of Graph Attention Networks PDF Video
Lovish Madaan and Siddhant Arora

Abstract: Graph Attention Networks (GATs) are widely used for Representation Learning in Graphs, but there is no proper study highlighting on what tasks GATs perform better than other models and why. In this appraisal paper, we aim to improve our understanding of GATs on a variety of tasks, including link prediction, multi-class node classification, and pairwise node classification on benchmark datasets. We also perform ablation studies on the various hyperparameters of GATs and try to reason about the importance of each of these in node classification and link prediction tasks. Our study offers insights into the effectiveness of GATs as compared to other techniques, and we make our code public so as to facilitate future exploration.

Keywords: representation learning, node embeddings in graph networks, machine learning for graphs, graph attention networks
@inproceedings{mlg2020_21,
title={GATCheck: A Detailed Analysis of Graph Attention Networks},
author={Lovish Madaan and Siddhant Arora},
booktitle={Proceedings of the 16th International Workshop on Mining and Learning with Graphs (MLG)},
year={2020}
}

Graph Clustering with Graph Neural Networks PDF Video
Anton Tsitsulin, John Palowitch, Bryan Perozzi and Emmanuel Müller

Abstract: Graph Neural Networks (GNNs) have achieved state-of-the-art results on many graph analysis tasks such as node classification and link prediction. However, important unsupervised problems on graphs, such as graph clustering, have proved more resistant to advances in GNNs. In this paper, we study unsupervised training of GNN pooling in terms of their clustering capabilities. We start by drawing a connection between graph clustering and graph pooling: intuitively, a good graph clustering is what one would expect from a GNN pooling layer. Counterintuitively, we show that this is not true for state-of-the-art pooling methods, such as MinCut pooling. To address these deficiencies, we introduce Deep Modularity Networks (DMoN), an unsupervised pooling method inspired by the modularity measure of clustering quality, and show how it tackles recovery of the challenging clustering structure of real-world graphs. In order to clarify the regimes where existing methods fail, we carefully design a set of experiments on synthetic data which show that DMoN is able to jointly leverage the signal from the graph structure and node attributes. Similarly, on real-world data, we show that DMoN produces high quality clusters which correlate strongly with ground truth labels, achieving state-of-the-art results.

Keywords: graph clustering, graph neural networks, graph convolutional networks, deep learning, attributed graph clustering
@inproceedings{mlg2020_42,
title={Graph Clustering with Graph Neural Networks},
author={Anton Tsitsulin, John Palowitch, Bryan Perozzi and Emmanuel Müller},
booktitle={Proceedings of the 16th International Workshop on Mining and Learning with Graphs (MLG)},
year={2020}
}

Graph Frequency Analysis of COVID-19 Incidence in the United States PDF Video
Yang Li and Gonzalo Mateos

Abstract: The COVID-19 pandemic markedly changed the way of life in the United States (US). From early isolated regional outbreaks to ongoing country-wise spread, the contagion exhibits different patterns at various timescales and locations. Thus, a close study of the COVID-19 spread patterns can offer valuable insights on how counties were affected by the virus. In the present work, a graph frequency analysis was conducted to investigate the spread pattern of COVID-19 in the US. A geographical graph was constructed by computing the geodesic distance between 3142 US counties. The numbers of daily confirmed COVID-19 cases per county were collected and represented as graph signals, then mapped into the frequency domain via the graph Fourier transform. The concept of graph frequency in Graph Signal Processing (GSP) enables the decomposition of graph signals (i.e. daily confirmed cases) into modes with smooth or rapid variations with respect to the underlying graph connectivity. Follow-up analysis revealed the relationship between graph frequency components and the COVID-19 spread pattern within and across counties. Specifically, our preliminary graph frequency analysis mined (and learned from) confirmed case counts to unveil spatio-temporal contagion patterns of COVID-19 incidence for each US county. Overall, results here support the promising prospect of using GSP tools for epidemiology knowledge discovery on graphs.

Keywords: Graph data mining, graph signal processing, frequency analysis, contagion pattern recognition
@inproceedings{mlg2020_24,
title={Graph Frequency Analysis of COVID-19 Incidence in the United States},
author={Yang Li and Gonzalo Mateos},
booktitle={Proceedings of the 16th International Workshop on Mining and Learning with Graphs (MLG)},
year={2020}
}

Graph Summarization and Graph Embeddings: Towards A Spectral Connection PDF Video
Arpit Merchant and Michael Mathioudakis

Abstract: The graph summarization problem is to define a compressed data structure that can concisely describe the original graph. A standard class of techniques for summarization involves grouping nodes into supernodes via aggregation or clustering such that the lp-reconstruction error, i.e. the p-norm between the original adjacency matrix and the adjacency matrix recovered from the compressed summary, is minimized. Our main result shows that graph summarization can be reformulated as a trace maximization problem, the relaxed version of which can be solved exactly by all the eigenvectors of the adjacency matrix. We also prove a lower bound on the optimal solution which uses k eigenvectors for a summary with k supernodes. Our results motivate a simple spectral clustering algorithm that can yield excellent summaries. Our experiments validate the quality of the resultant summaries.

Keywords: graph summarization, graph embeddings, spectral graph theory
@inproceedings{mlg2020_17,
title={Graph Summarization and Graph Embeddings: Towards A Spectral Connection},
author={Arpit Merchant and Michael Mathioudakis},
booktitle={Proceedings of the 16th International Workshop on Mining and Learning with Graphs (MLG)},
year={2020}
}

Graph-based State Representation for Deep Reinforcement Learning PDF Video
Vikram Waradpande, Daniel Kudenko and Megha Khosla

Abstract: Deep RL approaches build much of their success on the ability of the deep neural network to generate useful internal representations. Nevertheless, they suffer from a high sample-complexity and starting with a good input representation can have a significant impact on the performance. In this paper, we exploit the fact that the underlying Markov decision process (MDP) represents a graph, which enables us to incorporate the topological information for effective state representation learning. Motivated by the recent success of node representations for several graph analytical tasks we specifically investigate the capability of node representation learning methods to effectively encode the topology of the underlying MDP in Deep RL. To this end, we perform a comparative analysis of several models chosen from different classes of representation learning algorithms for policy learning in grid-world navigation tasks, which are representative of a large class of RL problems. We find that all embedding methods outperform the commonly used matrix representation of grid-world environments in all of the studied cases. Moreoever, graph convolution based methods are outperformed by simpler random walk based methods and graph linear autoencoders.

Keywords: Deep Reinforcement Learning, Deep Q-Learning, Markov Decision Processes, Unsupervised Node Embeddings, Graph Neural Networks, Graph representation
@inproceedings{mlg2020_20,
title={Graph-based State Representation for Deep Reinforcement Learning},
author={Vikram Waradpande, Daniel Kudenko and Megha Khosla},
booktitle={Proceedings of the 16th International Workshop on Mining and Learning with Graphs (MLG)},
year={2020}
}

Heterogeneous Threshold Estimation for Linear Threshold Modeling PDF Video
Christopher Tran and Elena Zheleva

Abstract: Social networks play a central role in the spread of diseases, ideas, and beliefs. The Linear Threshold Model (LTM) is a prominent model which describes the process of diffusion through the network and how nodes become "infected" based on a threshold of number of neighbors who are already "infected." LTM is often used with the assumption that node thresholds are globally unique or randomly distributed. In many cases, however, thresholds can differ between individuals, and knowing individual-level thresholds can lead to better diffusion predictions. In this work, we propose a causal inference approach for estimating node thresholds. We develop a Structural Causal Model to show the identifiability of causal effects in the Linear Threshold Model, and map the threshold estimation problem to heterogeneous treatment effect estimation. Through experimental results on real-world and synthetic datasets, we show that individualized thresholds play an important part in reliable long-term diffusion prediction.

Keywords: heterogeneous treatment effects, linear threshold model, causal inference, information diffusion
@inproceedings{mlg2020_23,
title={Heterogeneous Threshold Estimation for Linear Threshold Modeling},
author={Christopher Tran and Elena Zheleva},
booktitle={Proceedings of the 16th International Workshop on Mining and Learning with Graphs (MLG)},
year={2020}
}

Hop Sampling: A Simple Regularized Graph Learning for Non-Stationary Environments PDF Video
Young-Jin Park, Kyuyong Shin and Kyungmin Kim

Abstract: Graph representation learning is gaining popularity in a wide range of applications, such as social networks analysis, computational biology, and recommender systems. However, different with positive results from many academic studies, applying graph neural networks (GNNs) in a real-world application is still challenging due to non-stationary environments. The underlying distribution of streaming data changes unexpectedly, resulting in different graph structures (a.k.a., concept drift). Therefore, it is essential to devise a robust graph learning technique so that the model does not overfit to the training graphs. In this work, we present Hop Sampling, a straightforward regularization method that can effectively prevent GNNs from overfishing. The hop sampling randomly selects the number of propagation steps rather than fixing it, and by doing so, it encourages the model to learn meaningful node representation for all intermediate propagation layers and to experience a variety of plausible graphs that are not in the training set. Particularly, we describe the use case of our method in recommender systems, a representative example of the real-world non-stationary case. We evaluated hop sampling on a large-scale real-world LINE dataset and conducted an online A/B/n test in LINE Coupon recommender systems of LINE Wallet Tab. Experimental results demonstrate that the proposed scheme improves the prediction accuracy of GNNs. We observed hop sampling provides 7.97% and 16.93% improvements for NDCG and MAP compared to non-regularized GNN models in our online service. Furthermore, models using hop sampling alleviate the oversmoothing issue in GNNs enabling a deeper model as well as more diversified representation.

Keywords: Graph neural networks, Graph representation, Non-stationary graphs, Recommender System, Mobile Coupon Service
@inproceedings{mlg2020_43,
title={Hop Sampling: A Simple Regularized Graph Learning for Non-Stationary Environments},
author={Young-Jin Park, Kyuyong Shin and Kyungmin Kim},
booktitle={Proceedings of the 16th International Workshop on Mining and Learning with Graphs (MLG)},
year={2020}
}

Influence of Asymmetry and Structural Roles on Triad Patterns in Undirected Networks PDF Video
Milos Kudelka, Eliska Ochodkova, Jakub Plesnik and Sarka Zehnalova

Abstract: Triads, i.e., variously interconnected triplets of nodes, significantly affect the network structure. Closed triads, for instance, are the building blocks of communities. Our study focuses on the analysis of triads in which the ego is connected to two alters, with the alters not having to be connected; therefore, the triads that are studied need not be closed. The analysis uses two approaches based on asymmetric relationships between pairs of nodes. In the first approach, we work with three different node roles, in which the ego and its alters can appear in triads. We get a total of eighteen different role-based triad patterns. The second approach allows us to work with a total of four different types of ties and six different alter-pair patterns. In experiments with four different types of real-world networks, we show how the properties of these networks differ in terms of role-based triad patterns. In some of these networks, we further show that the triad-based properties remain stable during network growth. The main contribution of our paper is the use of asymmetric relations for the definition of four types of dependency-based tie strengths between nodes and the analysis of their influence on the occurrence of different triad-based patterns in networks.

Keywords: social network, triadic closure, structural role, asymmetric dependency, triad pattern
@inproceedings{mlg2020_18,
title={Influence of Asymmetry and Structural Roles on Triad Patterns in Undirected Networks},
author={Milos Kudelka, Eliska Ochodkova, Jakub Plesnik and Sarka Zehnalova},
booktitle={Proceedings of the 16th International Workshop on Mining and Learning with Graphs (MLG)},
year={2020}
}

Karate Club: An API Oriented Open-Source Python Framework for Unsupervised Learning on Graphs PDF Video
Benedek Rozemberczki, Olivér Kiss and Rik Sarkar

Abstract: Graphs encode important structural properties of complex systems. Machine learning on graphs has therefore emerged as an important technique in research and applications. We present Karate Club - a Python framework combining more than 30 state-of-the-art graph mining algorithms. These unsupervised techniques make it easy to identify and represent common graph features. The primary goal of the package is to make community detection, node and whole graph embedding available to a wide audience of machine learning researchers and practitioners. Karate Club is designed with an emphasis on a consistent application interface, scalability, ease of use, sensible out of the box model behaviour, standardized dataset ingestion, and output generation. This paper discusses the design principles behind the framework with practical examples. We show Karate Club's efficiency in learning performance on a wide range of real world clustering problems and classification tasks along with supporting evidence of its competitive speed.

Keywords: community detection, graph embedding, node embedding, graph classification, node classification, Python, implicit factorization, graph clustering
@inproceedings{mlg2020_8,
title={Karate Club: An API Oriented Open-Source Python Framework for Unsupervised Learning on Graphs},
author={Benedek Rozemberczki, Olivér Kiss and Rik Sarkar},
booktitle={Proceedings of the 16th International Workshop on Mining and Learning with Graphs (MLG)},
year={2020}
}

Learning Distributed Representations of Graphs with Geo2DR PDF Video
Paul Scherer and Pietro Lio

Abstract: We present Geo2DR (Geometric to Distributed Representations), a GPU ready Python library for unsupervised learning on graph-structured data using discrete substructure patterns and neural language models. It contains efficient implementations of popular graph decomposition algorithms and neural language models in PyTorch which can be combined to learn representations of graphs using the distributive hypothesis. Furthermore, Geo2DR comes with general data processing and loading methods to bring substantial speed-up in the training of the neural language models. Through this we provide a modular set of tools and building blocks to quickly construct methods capable of learning distributed representations of graphs. This is useful for replication of existing methods, modification, and development of completely new methods. This paper serves to present the Geo2DR library and perform a comprehensive comparative analysis of existing methods re-implemented using Geo2DR across widely used graph classification benchmarks. Geo2DR displays a high reproducibility of results of published methods and interoperability with other libraries useful for distributive language modelling, making it a useful addition to the graph representation learning toolkit.

Keywords: Graph Representation Learning, Research Toolkit, Reproducibility, Distributed Representations, Neural Language Model, Graph Kernel
@inproceedings{mlg2020_10,
title={Learning Distributed Representations of Graphs with Geo2DR},
author={Paul Scherer and Pietro Lio},
booktitle={Proceedings of the 16th International Workshop on Mining and Learning with Graphs (MLG)},
year={2020}
}

Learning Generic Representations for Dynamic Social Interaction PDF Video
Yanbang Wang, Pan Li, Chongyang Bai, V.S. Subrahmanian and Jure Leskovec

Abstract: Social interaction, such as eye contact, speaking and listening, are ubiquitous in our life and carries important clues of human's social status and psychological state. With evolving dynamics fundamentally different from people's social relationships, the complex interactions among a group of people are another informative resource to analyze patterns of humans' social behaviors and characteristics. Despite the great importance, previous approaches on extracting patterns from such dynamic social interactions are still underdeveloped and overly task-specific. We fill this gap by proposing a temporal network formulation of the problem, combined with a novel representation learning framework, temporal network-diffusion convolution networks (TNDCN) that accommodates the many downstream tasks with a unified structure: we creatively propagate people's fast-changing descriptive traits among their evolving gazing networks with specially designed (1) network diffusion scheme and (2) hierarchical pooling to learn high-quality embeddings for downstream tasks using a consistent structure and minimal feature engineering. Analysis show that (1) can not only capture patterns from existed interactions but also people's avoidance of interactions that turn out just as critical. (2) allows us to flexibly collect different fine-grained critical interaction features scattered over an extremely long time span, which is also key to success but essentially fails all the previous temporal GNNs based on recurrent structures. We evaluate our model over three different prediction tasks, detecting deception, dominance and nervousness. Our model not only consistently outperforms previous baselines but also provides good interpretation by implying two important pieces of social insight derived from the learned coefficients.

Keywords: Social Interaction, Dynamic Network, Representation Learning, Deception Detection
@inproceedings{mlg2020_6,
title={Learning Generic Representations for Dynamic Social Interaction},
author={Yanbang Wang, Pan Li, Chongyang Bai, V.S. Subrahmanian and Jure Leskovec},
booktitle={Proceedings of the 16th International Workshop on Mining and Learning with Graphs (MLG)},
year={2020}
}

Link Predictions in an Online Health Community for Smoking Cessation PDF Video
Sulyun Lee, Hankyu Jang, Kang Zhao, Michael S. Amato and Amanda L. Graham

Abstract: Effective link predictions in online social networks can help to improve user experience and engagement, which are often associated with better health outcomes for users of online health communities (OHCs). However, limited attention has been paid to predicting social network links in OHCs. This paper explores link predictions in an OHC for smoking cessation by considering it as a multi-relational social network that incorporates multiple types of social relationships. We demonstrate that leveraging information from multiple networks built based on different types of relationships is superior to using only information from a single network or the aggregated network. In addition, adding community structures and nodal similarities based on network embedding can help link predictions in different ways. Our work has implications for the design and management of a successful online health community.

Keywords: Social Network, Network Embedding, Multi-Relational Network, Supervised Learning, Smoking Cessation
@inproceedings{mlg2020_25,
title={Link Predictions in an Online Health Community for Smoking Cessation},
author={Sulyun Lee, Hankyu Jang, Kang Zhao, Michael S. Amato and Amanda L. Graham},
booktitle={Proceedings of the 16th International Workshop on Mining and Learning with Graphs (MLG)},
year={2020}
}

Little Ball of Fur: A Python Library for Graph Sampling PDF Video
Benedek Rozemberczki, Olivér Kiss and Rik Sarkar

Abstract: Sampling graphs is an important task in data mining. In this paper, we describe Little Ball of Fur a Python library that includes more than twenty graph sampling algorithms. Our goal is to make node, edge, and exploration-based network sampling techniques accessible to a large number of professionals, researchers, and students in a single streamlined framework. We created this framework with a focus on a coherent application public interface which has a convenient design, generic input data requirements, and reasonable baseline settings of algorithms. Here we overview these design foundations of the framework in detail with illustrative code snippets. We show the practical usability of the library by estimating various global statistics of social networks and web graphs. Experiments demonstrate that Little Ball of Fur can speed up node and whole graph embedding techniques considerably with mildly deteriorating the predictive value of distilled features.

Keywords: network sampling, graph sampling, subsampling, network analytics, graph mining, Python
@inproceedings{mlg2020_9,
title={Little Ball of Fur: A Python Library for Graph Sampling},
author={Benedek Rozemberczki, Olivér Kiss and Rik Sarkar},
booktitle={Proceedings of the 16th International Workshop on Mining and Learning with Graphs (MLG)},
year={2020}
}

MIDAS: Microcluster-Based Detector of Anomalies in Edge Streams PDF Video
Siddharth Bhatia, Bryan Hooi, Minji Yoon, Kijung Shin and Christos Faloutsos

Abstract: Given a stream of graph edges from a dynamic graph, how can we assign anomaly scores to edges in an online manner, for the purpose of detecting unusual behavior, using constant time and memory? Existing approaches aim to detect individually surprising edges. In this work, we propose MIDAS, which focuses on detecting microcluster anomalies, or suddenly arriving groups of suspiciously similar edges, such as lockstep behavior, including denial of service attacks in network traffic data. MIDAS has the following properties: (a) it detects microcluster anomalies while providing theoretical guarantees about its false positive probability; (b) it is online, thus processing each edge in constant time and constant memory, and also processes the data 162-644 times faster than state-of-the-art approaches; (c) it provides 42%-48% higher accuracy (in terms of AUC) than state-of-the-art approaches. Category: Relevant work that has been previously published at AAAI '20.

Keywords: Edge Streams, Microcluster, Dynamic Graphs, Anomaly Detection
@inproceedings{mlg2020_41,
title={MIDAS: Microcluster-Based Detector of Anomalies in Edge Streams },
author={Siddharth Bhatia, Bryan Hooi, Minji Yoon, Kijung Shin and Christos Faloutsos},
booktitle={Proceedings of the 16th International Workshop on Mining and Learning with Graphs (MLG)},
year={2020}
}

Mining Persistent Activity in Continually Evolving Networks PDF Video
Caleb Belth, Xinyi Zheng and Danai Koutra

Abstract: Work that will be presented at the main conference. Frequent pattern mining is a key area of study that gives insights into the structure and dynamics of evolving networks, such as social or road networks. However, not only does a network evolve, but often the way that it evolves, itself evolves. Thus, knowing, in addition to patterns' frequencies, for how long and how regularly they have occurred—i.e., their persistence—can add to our understanding of evolving networks. In this work, we propose the problem of mining activity that persists through time in continually evolving networks—i.e., activity that repeatedly and consistently occurs. We extend the notion of temporal motifs to capture activity among specific nodes, in what we call activity snippets, which are small sequences of edge-updates that reoccur. We propose axioms and properties that a measure of persistence should satisfy, and develop such a persistence measure. We also propose PENminer, an efficient framework for mining activity snippets' Persistence in Evolving Networks, and design both offline and streaming algorithms. We apply PENminer to numerous real, large-scale evolving networks and edge streams, and find activity that is surprisingly regular over a long period of time, but too infrequent to be discovered by aggregate count alone, and bursts of activity exposed by their lack of persistence. Our findings with PENminer include neighborhoods in NYC where taxi traffic persisted through Hurricane Sandy, the opening of new bike-stations, characteristics of social network users, and more. Moreover, we use PENminer to identify subtle anomalies in a bike network and catch attacks in an IP-network, outperforming baselines by 8% in AUC.

Keywords: Persistence, Evolving Networks, Edge Streams
@inproceedings{mlg2020_14,
title={Mining Persistent Activity in Continually Evolving Networks},
author={Caleb Belth, Xinyi Zheng and Danai Koutra},
booktitle={Proceedings of the 16th International Workshop on Mining and Learning with Graphs (MLG)},
year={2020}
}

Network Embedding with Attribute Refinement PDF Video
Tong Xiao, Yuan Fang, Hongxia Yang and Vincent W. Zheng

Abstract: Network embedding has been an active research area given its effectiveness in mapping nodes to low-dimensional representations. While previous studies mostly focus on network topology, recent advances have shown that rich node-level information, known as attributes, often exist and can substantially benefit embedding based on the assumption of homophily: nodes often connect to other nodes similar to themselves. However, we find that inconsistencies often occur in node attributes in real-world data, which can undermine the homophily assumption and thus degrade the performance of attributed network embedding. To address this drawback, in this paper, we present a novel framework for unsupervised network embedding with attribute refinement. In particular, we propose a learnable filter to automatically refine the individual attributes of every node. To overcome the challenge of no supervision, we leverage homophily to guide the refinement—attributes should be fine-tuned in a way to reinforce the correlation with topology. Finally, we conduct extensive experiments on three benchmark real-world datasets, which show that our model significantly outperforms state-of-the-art methods on both node classification and link prediction tasks. Furthermore, we perform model analysis to demonstrate that our framework can effectively refine attributes.

Keywords: Network embedding, attribute refinement, homophily
@inproceedings{mlg2020_19,
title={Network Embedding with Attribute Refinement},
author={Tong Xiao, Yuan Fang, Hongxia Yang and Vincent W. Zheng},
booktitle={Proceedings of the 16th International Workshop on Mining and Learning with Graphs (MLG)},
year={2020}
}

Network Experiment Design for Estimating Direct Treatment Effects PDF Video
Zahra Fatemi and Elena Zheleva

Abstract: Network experiment design refers to the design of controlled experiments for interacting units with the goal of estimating a causal effect of interest. Estimating the effect of treatment alone on units' outcome, known as direct treatment effect, in network experiments is challenging due to information spillover between peers through shared edges. Prominent methods for network experiment design mostly focus on estimating total treatment effects, the combination of peer effects and direct treatment effects. Less focus has been given to approaches that provide an unbiased estimation of direct treatment effect. We present a framework that takes advantage of independent sets and assigns treatment and control only to a set of non-adjacent nodes in a graph, in order to disentangle peer effects from direct treatment effect estimation. Randomizing over independent set nodes removes peer effects between nodes in the experiment while canceling out the peer effects from nodes outside the experiment. Through a series of simulated experiments on synthetic and real-world network datasets, we show that our framework significantly increases the accuracy of direct treatment effect estimation in network experiments.

Keywords: Causal Inference, Network experiment design, Peer effect, Direct treatment effect
@inproceedings{mlg2020_34,
title={Network Experiment Design for Estimating Direct Treatment Effects},
author={Zahra Fatemi and Elena Zheleva},
booktitle={Proceedings of the 16th International Workshop on Mining and Learning with Graphs (MLG)},
year={2020}
}

On Structural vs. Proximity-based Temporal Node Embeddings PDF Video
Puja Trivedi, Alican Büyükçakır, Yin Lin, Yinlong Qian, Di Jin and Danai Koutra

Abstract: We investigate the representation power of static node embeddings in dynamic or temporal settings. To this end, we introduce a framework that incorporates different design options for extending static node embeddings to temporal settings: temporal combination schemes to introduce dynamics in otherwise static approaches, alignment methods that lead to comparability of embedding dimensions across time steps, and different edge operators for generating edge embeddings from node embeddings. In our empirical analysis, we evaluate the performance of both proximity-based and structural node embedding methods in a temporal link prediction task over four time-evolving networks. Our results show that proper choice over these designs yields up to 20% absolute improvement over baselines that do not leverage temporal combination and embedding alignment. We further present broad trends to guide design decisions for embedding methods in temporal settings.

Keywords: temporal graphs, graph embeddings, temporal link prediction
@inproceedings{mlg2020_37,
title={On Structural vs. Proximity-based Temporal Node Embeddings},
author={Puja Trivedi, Alican Büyükçakır, Yin Lin, Yinlong Qian, Di Jin and Danai Koutra},
booktitle={Proceedings of the 16th International Workshop on Mining and Learning with Graphs (MLG)},
year={2020}
}

Robust Unsupervised Mining of Dense Sub-Graphs at Multiple Resolutions PDF Video
Neil Gupta, Gunjan Gupta, Joydeep Ghosh, Sheshank Shankar and Alex Mallen

Abstract: Category: Novel research paper. Whereas in traditional partitional clustering, each data point belongs to a cluster, there are several applications where only some of the points form relatively homogenous or "dense" groups, and points that don't seem to belong to any cluster need to be ignored. Moreover, different clusters may emerge at different scales or density levels. This makes it difficult to identify them using a single density threshold, especially if we also want to ignore the non-clustering data. If data is represented in a metric space, then recent extensions of a classical approach called Hierarchical Mode Analysis (HMA) are able to identify clusters at multiple resolutions, while ignoring "non-dense" areas. However, this approach does not apply when the relations between pairs of data points can only be represented as a (sparse) similarity or affinity graph. Motivated by two complex, real-life applications where one needs to identify dense subgraphs at multiple resolutions, while ignoring nodes that are not well connected in the similarity graph, we introduce a novel algorithm called HIMAG (Hierarchical Incremental Mode Analysis for Graphs) that provides capabilities analogous to HMA based methods but applicable to graphs. We also provide a powerful multi-resolution visualization tool customized for the new algorithm. We present results on the two motivating real-world applications as well as two standard benchmark social graph datasets, to show the power of our approach and compare it with some standard graph partitioning algorithms that were retrofitted to produce dense clusters by pruning non-dense data in a non-trivial manner. We are also open-sourcing the new dense graph datasets and tools to the community.

Keywords: graph learning, dense graphs, hierarchical clusters, visualization, automated cluster selection, social graphs, hierarchical mode analysis
@inproceedings{mlg2020_36,
title={Robust Unsupervised Mining of Dense Sub-Graphs at Multiple Resolutions},
author={Neil Gupta, Gunjan Gupta, Joydeep Ghosh, Sheshank Shankar and Alex Mallen},
booktitle={Proceedings of the 16th International Workshop on Mining and Learning with Graphs (MLG)},
year={2020}
}

SNAPSKETCH: Graph Representation Approach for Intrusion Detection in a Streaming Graph PDF Video
Ramesh Paudel and William Eberle

Abstract: In this paper, we propose a novel unsupervised graph representation approach in a graph stream called SNAPSKETCH that can be used for anomaly detection. It first performs a fixed-length random walk from each node in a network and constructs n-shingles from a walk path. The top discriminative n-shingles identified using a frequency measure are projected into a dimensional projection vector chosen uniformly at random. Finally, a graph is sketched into a low-dimensional sketch vector using a simplified hashing of the projection vector and the cost of shingles. Using the learned sketch vector, anomaly detection is done using the state-of-the-art anomaly detection approach called RRCF [1]. SNAPSKETCH has several advantages, including fully unsupervised learning, constant memory space usage, entire-graph embedding, and real-time anomaly detection.

Keywords: Graph Representation, Anomaly Detection, DoS Attack, Intrusion Detection, Graph Sketching
@inproceedings{mlg2020_1,
title={SNAPSKETCH: Graph Representation Approach for Intrusion Detection in a Streaming Graph},
author={Ramesh Paudel and William Eberle},
booktitle={Proceedings of the 16th International Workshop on Mining and Learning with Graphs (MLG)},
year={2020}
}

Scalable and Consistent Estimation in Continuous-time Networks of Relational Events PDF Video
Makan Arastuie, Subhadeep Paul and Kevin S. Xu

Abstract: In many application settings involving networks, such as messages between users of an on-line social network or transactions between traders in financial markets, the observed data consist of timestamped relational events, which form a continuous-time network. We propose the Community Hawkes Independent Pairs (CHIP) generative model for such networks. We show that applying spectral clustering to adjacency matrices constructed from relational events generated by the CHIP model provides consistent community detection for a growing number of nodes. We also develop consistent and computationally efficient estimators for the model parameters. We demonstrate that our proposed CHIP model and estimation procedure scales to large networks with tens of thousands of nodes and provides superior fits than existing continuous-time network models on several real networks. This submission is a novel research paper.

Keywords: Community Hawkes Independent Pairs model, event-based network, continuous-time network, timestamped network, relational events, spectral clustering, Hawkes process
@inproceedings{mlg2020_27,
title={Scalable and Consistent Estimation in Continuous-time Networks of Relational Events},
author={Makan Arastuie, Subhadeep Paul and Kevin S. Xu},
booktitle={Proceedings of the 16th International Workshop on Mining and Learning with Graphs (MLG)},
year={2020}
}

Scale-Free, Attributed and Class-Assortative Graph Generation to Facilitate Introspection of Graph Neural Networks PDF Video
Neil Shah

Abstract: Semi-supervised node classification on graphs is a complex interplay between graph structure, node features and class-assortative (homophily) properties, and the flexibility of a model to capture these nuances. Modern datasets used to push the frontier for these tasks exhibit diverse properties across these fronts, making it challenging to study how these properties individually and jointly influence performance of modern embedding-based methods like graph neural networks (GNNs) for this task. In this work-in-progress, we propose an intuitive and flexible scale-free graph generation model, CaBaM, which enables simulation of class-assortative and attributed graphs via the well-known Barabasi-Albert model. We show empirically and theoretically how our model can easily describe a variety of graph types, while imbuing the generated graphs with the necessary ingredients for attribute, topology, and label-aware semi-supervised node-classification.

Keywords: semi-supervised learning, graph generation, barabasi albert, graphs
@inproceedings{mlg2020_33,
title={Scale-Free, Attributed and Class-Assortative Graph Generation to Facilitate Introspection of Graph Neural Networks},
author={Neil Shah},
booktitle={Proceedings of the 16th International Workshop on Mining and Learning with Graphs (MLG)},
year={2020}
}

Substitution Techniques for Grocery Fulfillment and Assortment Optimization Using Product Graphs PDF Video
Amit Pande, Aparupa Das Gupta, Kai Ni, Rahul Biswas and Sayon Majumdar

Abstract: Identifying substitutable pairs or groups of products is key to building relevant product assortment for brick-and-mortar stores as well as to efficiently handle out of stock scenarios. In this work, we describe the unique challenges with a retailer’s data to identify substitutable product pairs from a large catalog and nationwide store transactions. We apply some of the well established approaches in data mining and machine learning to customer store purchase data and product attributes data to generate networks of substitutable products. This paper presents a novel application of substitutable product networks integrated with an assortment optimization engine that was developed in-house to select the optimal assortment of products for the stores. The outcomes from a large scale experiment conducted across 120 stores within United States demonstrates a unit sale lift in excess of 11%. In another set of tests, we analyze the performance of various algorithms for product substitution and fulfillment when customers encounter Out of Stock (OOS) scenario while shopping groceries for same day delivery.

Keywords: Product Substitutes, Data Mining, Graph, Assortment Optimization
@inproceedings{mlg2020_7,
title={Substitution Techniques for Grocery Fulfillment and Assortment Optimization Using Product Graphs},
author={Amit Pande, Aparupa Das Gupta, Kai Ni, Rahul Biswas and Sayon Majumdar},
booktitle={Proceedings of the 16th International Workshop on Mining and Learning with Graphs (MLG)},
year={2020}
}

TIES: Temporal Interaction Embeddings For Enhancing Social Media Integrity At Facebook PDF Video
Nima Noorshams, Saurabh Verma and Aude Hofleitner

Abstract: Since its inception, Facebook has become an integral part of the online social community. People rely on Facebook to connect with others and build communities. As a result, it is paramount to protect the integrity of such a large network in a fast and scalable manner. In this paper, we present our efforts to protect various social media entities at Facebook from people who try to abuse our platform. We present a novel Temporal Interaction EmbeddingS (TIES) model that is designed to capture rogue social interactions and flag them for further suitable actions. TIES is a supervised, deep learning, production ready model at Facebook-scale networks. Prior works on integrity problems are mostly focused on capturing either only static or certain dynamic features of social entities. In contrast, TIES can capture both these variant behaviors in a unified model owing to the recent strides made in the domains of graph embedding and deep sequential pattern learning. To show the real-world impact of TIES, we present a few applications especially for preventing spread of misinformation, fake account detection, and reducing ads payment risks in order to enhance Facebook platform’s integrity.

Keywords: temporal embeddings, sequence modeling, social media integrity
@inproceedings{mlg2020_38,
title={TIES: Temporal Interaction Embeddings For Enhancing Social Media Integrity At Facebook},
author={Nima Noorshams, Saurabh Verma and Aude Hofleitner},
booktitle={Proceedings of the 16th International Workshop on Mining and Learning with Graphs (MLG)},
year={2020}
}

Understanding and Evaluating Structural Node Embeddings PDF Video
Junchen Jin, Mark Heimann, Di Jin and Danai Koutra

Abstract: While most network embedding techniques model the proximity between nodes in a network, recently there has been significant interest in structural embeddings that are based on node equivalences, a notion rooted in sociology: equivalences or positions are collections of nodes that have similar roles—i.e., similar functions, ties or interactions with nodes in other positions—irrespective of their distance or reachability in the network. Unlike the proximity-based methods that are rigorously evaluated in the literature, the evaluation of structural embeddings is less mature. or real networks with labels that are arbitrarily defined, and its connection to sociological equivalences has hitherto been vague and tenuous. To fill in this gap, we set out to understand what types of equivalences structural embeddings capture. We are the first to contribute rigorous intrinsic and extrinsic evaluation methodology for structural embeddings, along with carefully-designed, diverse datasets of varying sizes. We observe a number of different evaluation variables that can lead to different results (e.g., choice of similarity measure or label definitions). We find that degree distributions within nodes’ local neighborhoods can lead to simple yet effective baselines. We hope that our findings can influence the design of further node embedding methods and also pave the way for future evaluation of existing methods. **Category: appraisal paper**

Keywords: structural node embeddings, network embedding, role equivalence, intrinsic evaluation, extrinsic evaluation
@inproceedings{mlg2020_29,
title={Understanding and Evaluating Structural Node Embeddings},
author={Junchen Jin, Mark Heimann, Di Jin and Danai Koutra},
booktitle={Proceedings of the 16th International Workshop on Mining and Learning with Graphs (MLG)},
year={2020}
}

Unsupervised Hierarchical Graph Representation Learning by Mutual Information Maximization PDF Video
Fei Ding, Xiaohong Zhang, Justin Sybrandt and Ilya Safro

Abstract: Graph representation learning based on graph neural networks (GNNs) can greatly improve the performance of downstream tasks, such as node and graph classification. However, the general GNN models do not aggregate node information in a hierarchical manner, and can miss key higher-order structural features of many graphs. The hierarchical aggregation also enables the graph representations to be explainable. In addition, supervised graph representation learning requires labeled data, which is expensive and error-prone. To address these issues, we present an unsupervised graph representation learning method, Unsupervised Hierarchical Graph Representation (UHGR), which can generate hierarchical representations of graphs. Our method focuses on maximizing mutual information between "local" and high-level "global" representations, which enables us to learn the node embeddings and graph embeddings without any labeled data. To demonstrate the effectiveness of the proposed method, we perform the node and graph classification using the learned node and graph embeddings. The results show that the proposed method achieves comparable results to state-of-the-art supervised methods on several benchmarks. In addition, our visualization of hierarchical representations indicates that our method can capture meaningful and interpretable clusters.

Keywords: graph neural networks, representation learning, unsupervised learning, hierarchical representation, mutual information
@inproceedings{mlg2020_13,
title={Unsupervised Hierarchical Graph Representation Learning by Mutual Information Maximization},
author={Fei Ding, Xiaohong Zhang, Justin Sybrandt and Ilya Safro},
booktitle={Proceedings of the 16th International Workshop on Mining and Learning with Graphs (MLG)},
year={2020}
}

Call for Papers

Due to public health concerns in light of the unfolding COVID-19 outbreak. We will follow exactly the option that ACM SIGKDD and the KDD 2020 organizing committee will suggest and will follow the style that the KDD conference adopts. Please check this website regularly for updates.

This workshop is a forum for exchanging ideas and methods for mining and learning with graphs, developing new common understandings of the problems at hand, sharing of data sets where applicable, and leveraging existing knowledge from different disciplines. The goal is to bring together researchers from academia, industry, and government, to create a forum for discussing recent advances in graph analysis. In doing so, we aim to better understand the overarching principles and the limitations of our current methods and to inspire research on new algorithms and techniques for mining and learning with graphs.

To reflect the broad scope of work on mining and learning with graphs, we encourage submissions that span the spectrum from theoretical analysis to algorithms and implementation, to applications and empirical studies. As an example, the growth of user-generated content on blogs, microblogs, discussion forums, product reviews, etc., has given rise to a host of new opportunities for graph mining in the analysis of social media. We encourage submissions on theory, methods, and applications focusing on a broad range of graph-based approaches in various domains.

Topics of interest include, but are not limited to:

  • Theoretical aspects:
    • Computational or statistical learning theory related to graphs
    • Theoretical analysis of graph algorithms or models
    • Sampling and evaluation issues in graph algorithms
    • Analysis of dynamic graphs
  • Algorithms and methods:
    • Graph mining
    • Probabilistic and graphical models for structured data
    • Heterogeneous/multi-model graph analysis
    • Network embedding models
    • Statistical models of graph structure
    • Combinatorial graph methods
    • Semi-supervised learning, active learning, transductive inference, and transfer learning in the context of graph
  • Applications and analysis:
    • Analysis of social media
    • Analysis of biological networks
    • Knowledge graph construction
    • Large-scale analysis and modeling

We welcome many kinds of papers, such as, but not limited to:

  • Novel research papers
  • Demo papers
  • Work-in-progress papers
  • Visionary papers (white papers)
  • Appraisal papers of existing methods and tools (e.g., lessons learned)
  • Relevant work that has been previously published
  • Work that will be presented at the main conference

Authors should clearly indicate in their abstracts the kinds of submissions that the papers belong to, to help reviewers better understand their contributions.
All papers will be peer reviewed, single-blinded. Submissions must be in PDF, no more than 8 pages long — shorter papers are welcome — and formatted according to the standard double-column ACM Proceedings Style.
The accepted papers will be published on the workshop’s website and will not be considered archival for resubmission purposes.
Authors whose papers are accepted to the workshop will have the opportunity to participate in a spotlight and poster session, and some set will also be chosen for oral presentation.

For paper submission, please proceed to the submission website.

This year MLG will be held jointly with the International Workshop on Deep Learning on Graphs (KDD-DLG). DLG will maintain a separate submission website and program committee.

Please send enquiries to chair@mlgworkshop.org.

To receive updates about the current and future workshops and the Graph Mining community, please join the Mailing List, or follow the Twitter Account.

Important Dates

 

Paper Submission Deadline: June 15, 2020

Author Notification: July 15, 2020

Camera Ready: August 1, 2020

Workshop: August 24, 2020

Workshop Organizers

 
Shobeir Fakhraei

Shobeir Fakhraei

Machine Learning Scientist
Amazon

Aude Hofleitner

Aude Hofleitner

Research Scientist Manager
Facebook

Julian McAuley

Julian McAuley

Associate Professor
University of California San Diego

Bryan Perozzi

Bryan Perozzi

Research Scientist
Google Research
 

Tim Weninger

Tim Weninger

Associate Professor
University of Notre Dame

 

Program Committee

 

Aris Anagnostopoulos (Sapienza University of Rome)
Ana Paula Appel (IBM Research Brazil)
Christian Bauckhage (Fraunhofer)
Austin Benson (Cornell University)
Siddharth Bhatia (National University of Singapore)
Aleksandar Bojchevski (Technical University of Munich)
Ulf Brefeld (Leuphana Universität Lüneburg)
Marco Bressan (Sapienza University of Rome)
Ivan Brugere (University of Illinois at Chicago)
Ting Chen (University of California, Los Angeles)
Hocine Cherifi (University of Burgundy)
Aaron Clauset (University of Colorado Boulder)
Alessandro Epasto (Google)
Dhivya Eswaran (Amazon)
Yuan Fang (Singapore Management University)
William Hamilton (Stanford University)
Larry Holder (Washington State University)

Bryan Hooi (National University of Singapore)
Jin Kyu Kim (Facebook)
Danai Koutra (University of Michigan)
Stefano Leucci (University of L'Aquila)
Jundong Li (University of Virginia)
Fred Morstatter (University of Southern California)
Blaz Novak (Jozef Stefan Institute)
John Palowitch (Google)
Evangelos Papalexakis (University of California Riverside)
Ali Pinar (Sandia National Laboratories)
Jan Ramon (INRIA)
Sucheta Soundarajan (Syracuse University)
Acar Tamersoy (NortonLifeLock Research Group)
Hanghang Tong (University of Illinois at UC)
Stefan Wrobel (Fraunhofer IAIS & Univ. of Bonn)
Xin-Zeng Wu (Information Sciences Institute)
Zhongfei Zhang (SUNY Binghamton)

 

Previous Workshops