Held in conjunction with KDD'17

Aug 14, 2017 - Halifax, Nova Scotia, Canada

Aug 14, 2017 - Halifax, Nova Scotia, Canada

13th International Workshop on

Mining and Learning with Graphs

Mining and Learning with Graphs

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, 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.

Morning Sessions | |
---|---|

8:50 am | Opening Remarks |

9:00 am | Keynote: Nitesh Chawla Representing, Modeling, and Visualizing Higher Order Networks |

9:40 am | Paper Spotlights 1 |

10:00 am | Coffee Break |

10:30 am | Paper Spotlights 2 |

10:50 am | Keynote: Vahab Mirrokni Distributed Graph Mining: Theory and Practice |

11:30 am | Poster Session |

12:00 pm | Lunch (+ Poster Session) |

Afternoon Sessions | |
---|---|

1:00 pm | Keynote: Elena Zheleva Sharing and Gifting Online |

1:40 pm |
Contributed Talks 1: - HARP: Hierarchical Representation Learning for Networks (Bryan Perozzi) - Neural Embeddings of Graphs in Hyperbolic Space (Ben Chamberlain) |

2:00 pm | Keynote: Yan Liu Robust Diffusion Network Inference |

2:40 pm |
Contributed Talks 2: - A/B Testing in Networks with Adversarial Members (Kaleigh Clary) - A task-driven approach to time scale detection in dynamic networks (Benjamin Fish) |

3:00 pm | Coffee Break |

3:30 pm | Keynote: Jiliang Tang Node Relevance in Signed Networks: Measurements and Applications |

4:10 pm | Keynote: Zhenhui Jessie Li Mining Mobility Flow for Urban Computing |

4:50 pm | Closing Remarks |

Professor

University of Notre Dame

Associate Professor

Pennsylvania State University

Associate Professor

University of Southern California

Principal Scientist & Research Director

Google Research

Assistant Professor

Michigan State University

Assistant Professor

University of Illinois at Chicago

**HARP: Hierarchical Representation Learning for Networks**
PDF

*Haochen Chen, Bryan Perozzi, Yifan Hu and Steven Skiena*

@inproceedings{mlg2017_10,

title={HARP: Hierarchical Representation Learning for Networks},

author={Haochen Chen, Bryan Perozzi, Yifan Hu and Steven Skiena},

booktitle={Proceedings of the 13th International Workshop on Mining and Learning with Graphs (MLG)},

year={2017}

}

title={HARP: Hierarchical Representation Learning for Networks},

author={Haochen Chen, Bryan Perozzi, Yifan Hu and Steven Skiena},

booktitle={Proceedings of the 13th International Workshop on Mining and Learning with Graphs (MLG)},

year={2017}

}

**Neural Embeddings of Graphs in Hyperbolic Space**
PDF

*Ben Chamberlain, Marc Deisenroth and James Clough*

@inproceedings{mlg2017_6,

title={Neural Embeddings of Graphs in Hyperbolic Space},

author={Ben Chamberlain, Marc Deisenroth and James Clough},

booktitle={Proceedings of the 13th International Workshop on Mining and Learning with Graphs (MLG)},

year={2017}

}

title={Neural Embeddings of Graphs in Hyperbolic Space},

author={Ben Chamberlain, Marc Deisenroth and James Clough},

booktitle={Proceedings of the 13th International Workshop on Mining and Learning with Graphs (MLG)},

year={2017}

}

**A/B Testing in Networks with Adversarial Members**
PDF

*Kaleigh Clary and David Jensen*

@inproceedings{mlg2017_27,

title={A/B Testing in Networks with Adversarial Members},

author={Kaleigh Clary and David Jensen},

booktitle={Proceedings of the 13th International Workshop on Mining and Learning with Graphs (MLG)},

year={2017}

}

title={A/B Testing in Networks with Adversarial Members},

author={Kaleigh Clary and David Jensen},

booktitle={Proceedings of the 13th International Workshop on Mining and Learning with Graphs (MLG)},

year={2017}

}

**A task-driven approach to time scale detection in dynamic networks**
PDF

*Benjamin Fish and Rajmonda S. Caceres*

@inproceedings{mlg2017_17,

title={A task-driven approach to time scale detection in dynamic networks},

author={Benjamin Fish and Rajmonda S. Caceres},

booktitle={Proceedings of the 13th International Workshop on Mining and Learning with Graphs (MLG)},

year={2017}

}

title={A task-driven approach to time scale detection in dynamic networks},

author={Benjamin Fish and Rajmonda S. Caceres},

booktitle={Proceedings of the 13th International Workshop on Mining and Learning with Graphs (MLG)},

year={2017}

}

**A Temporal Tree Decomposition for Generating Temporal Graphs**
PDF

*Corey Pennycuff and Tim Weninger*

@inproceedings{mlg2017_7,

title={A Temporal Tree Decomposition for Generating Temporal Graphs},

author={Corey Pennycuff and Tim Weninger},

booktitle={Proceedings of the 13th International Workshop on Mining and Learning with Graphs (MLG)},

year={2017}

}

title={A Temporal Tree Decomposition for Generating Temporal Graphs},

author={Corey Pennycuff and Tim Weninger},

booktitle={Proceedings of the 13th International Workshop on Mining and Learning with Graphs (MLG)},

year={2017}

}

**Star sampling with and without replacement**
PDF

*Jonathan Stokes and Steven Weber*

@inproceedings{mlg2017_8,

title={Star sampling with and without replacement},

author={Jonathan Stokes and Steven Weber},

booktitle={Proceedings of the 13th International Workshop on Mining and Learning with Graphs (MLG)},

year={2017}

}

title={Star sampling with and without replacement},

author={Jonathan Stokes and Steven Weber},

booktitle={Proceedings of the 13th International Workshop on Mining and Learning with Graphs (MLG)},

year={2017}

}

**Fast Algorithms for Learning Latent Variables in Graphical Models**
PDF

*Mohammadreza Soltani and Chinmay Hegde*

@inproceedings{mlg2017_12,

title={Fast Algorithms for Learning Latent Variables in Graphical Models},

author={Mohammadreza Soltani and Chinmay Hegde},

booktitle={Proceedings of the 13th International Workshop on Mining and Learning with Graphs (MLG)},

year={2017}

}

title={Fast Algorithms for Learning Latent Variables in Graphical Models},

author={Mohammadreza Soltani and Chinmay Hegde},

booktitle={Proceedings of the 13th International Workshop on Mining and Learning with Graphs (MLG)},

year={2017}

}

**Parallel Graph Summarization for Knowledge Search**
PDF

*Qi Song, Mohammad Hossein Namaki, Peng Lin and Yinghui Wu*

@inproceedings{mlg2017_16,

title={Parallel Graph Summarization for Knowledge Search},

author={Qi Song, Mohammad Hossein Namaki, Peng Lin and Yinghui Wu},

booktitle={Proceedings of the 13th International Workshop on Mining and Learning with Graphs (MLG)},

year={2017}

}

title={Parallel Graph Summarization for Knowledge Search},

author={Qi Song, Mohammad Hossein Namaki, Peng Lin and Yinghui Wu},

booktitle={Proceedings of the 13th International Workshop on Mining and Learning with Graphs (MLG)},

year={2017}

}

**GraphZip: Mining Graph Streams using Dictionary-based Compression**
PDF

*Charles Packer and Larry Holder*

@inproceedings{mlg2017_18,

title={GraphZip: Mining Graph Streams using Dictionary-based Compression},

author={Charles Packer and Larry Holder},

booktitle={Proceedings of the 13th International Workshop on Mining and Learning with Graphs (MLG)},

year={2017}

}

title={GraphZip: Mining Graph Streams using Dictionary-based Compression},

author={Charles Packer and Larry Holder},

booktitle={Proceedings of the 13th International Workshop on Mining and Learning with Graphs (MLG)},

year={2017}

}

**Matrix Factorization with Side and Higher Order Information**
PDF

*Vatsal Shah, Nikhil Rao and Weicong Ding*

@inproceedings{mlg2017_19,

title={Matrix Factorization with Side and Higher Order Information},

author={Vatsal Shah, Nikhil Rao and Weicong Ding},

booktitle={Proceedings of the 13th International Workshop on Mining and Learning with Graphs (MLG)},

year={2017}

}

title={Matrix Factorization with Side and Higher Order Information},

author={Vatsal Shah, Nikhil Rao and Weicong Ding},

booktitle={Proceedings of the 13th International Workshop on Mining and Learning with Graphs (MLG)},

year={2017}

}

**graph2vec: Learning Distributed Representations of Graphs**
PDF

*Annamalai Narayanan, Mahinthan Chandramohan, Rajasekar Venkatesan, Lihui Chen, Yang Liu and Shantanu Jaiswal*

@inproceedings{mlg2017_21,

title={graph2vec: Learning Distributed Representations of Graphs},

author={Annamalai Narayanan, Mahinthan Chandramohan, Rajasekar Venkatesan, Lihui Chen, Yang Liu and Shantanu Jaiswal},

booktitle={Proceedings of the 13th International Workshop on Mining and Learning with Graphs (MLG)},

year={2017}

}

title={graph2vec: Learning Distributed Representations of Graphs},

author={Annamalai Narayanan, Mahinthan Chandramohan, Rajasekar Venkatesan, Lihui Chen, Yang Liu and Shantanu Jaiswal},

booktitle={Proceedings of the 13th International Workshop on Mining and Learning with Graphs (MLG)},

year={2017}

}

**Graphs for Malware Detection: The Next Frontier**
PDF

*Abhishek Sharma and B. Aditya Prakash*

@inproceedings{mlg2017_23,

title={Graphs for Malware Detection: The Next Frontier},

author={Abhishek Sharma and B. Aditya Prakash},

booktitle={Proceedings of the 13th International Workshop on Mining and Learning with Graphs (MLG)},

year={2017}

}

title={Graphs for Malware Detection: The Next Frontier},

author={Abhishek Sharma and B. Aditya Prakash},

booktitle={Proceedings of the 13th International Workshop on Mining and Learning with Graphs (MLG)},

year={2017}

}

**Location-based Event Detection Using Geotagged Semantic Graphs**
PDF

*Yifang Wei and Lisa Singh*

@inproceedings{mlg2017_24,

title={Location-based Event Detection Using Geotagged Semantic Graphs},

author={Yifang Wei and Lisa Singh},

booktitle={Proceedings of the 13th International Workshop on Mining and Learning with Graphs (MLG)},

year={2017}

}

title={Location-based Event Detection Using Geotagged Semantic Graphs},

author={Yifang Wei and Lisa Singh},

booktitle={Proceedings of the 13th International Workshop on Mining and Learning with Graphs (MLG)},

year={2017}

}

**Compressive Sampling for Sparse Recovery in Networks**
PDF

*Elahe Ghalebi K., Hamidreza Mahyar, Radu Grosu and Hamid R. Rabiee*

@inproceedings{mlg2017_29,

title={Compressive Sampling for Sparse Recovery in Networks},

author={Elahe Ghalebi K., Hamidreza Mahyar, Radu Grosu and Hamid R. Rabiee},

booktitle={Proceedings of the 13th International Workshop on Mining and Learning with Graphs (MLG)},

year={2017}

}

title={Compressive Sampling for Sparse Recovery in Networks},

author={Elahe Ghalebi K., Hamidreza Mahyar, Radu Grosu and Hamid R. Rabiee},

booktitle={Proceedings of the 13th International Workshop on Mining and Learning with Graphs (MLG)},

year={2017}

}

**Fraud Detection using Graph Topology and Temporal Spikes**
PDF

*Shenghua Liu, Bryan Hooi and Christos Faloutsos*

@inproceedings{mlg2017_3,

title={Fraud Detection using Graph Topology and Temporal Spikes},

author={Shenghua Liu, Bryan Hooi and Christos Faloutsos},

booktitle={Proceedings of the 13th International Workshop on Mining and Learning with Graphs (MLG)},

year={2017}

}

title={Fraud Detection using Graph Topology and Temporal Spikes},

author={Shenghua Liu, Bryan Hooi and Christos Faloutsos},

booktitle={Proceedings of the 13th International Workshop on Mining and Learning with Graphs (MLG)},

year={2017}

}

**Adaptive Candidate Generation for Scalable Edge-discovery Tasks on Data Graphs**
PDF

*Mayank Kejriwal*

@inproceedings{mlg2017_14,

title={Adaptive Candidate Generation for Scalable Edge-discovery Tasks on Data Graphs},

author={Mayank Kejriwal},

booktitle={Proceedings of the 13th International Workshop on Mining and Learning with Graphs (MLG)},

year={2017}

}

title={Adaptive Candidate Generation for Scalable Edge-discovery Tasks on Data Graphs},

author={Mayank Kejriwal},

booktitle={Proceedings of the 13th International Workshop on Mining and Learning with Graphs (MLG)},

year={2017}

}

**Cluster Hire in a Network of Experts**
PDF

*Meet Patel and Mehdi Kargar*

@inproceedings{mlg2017_22,

title={Cluster Hire in a Network of Experts},

author={Meet Patel and Mehdi Kargar},

booktitle={Proceedings of the 13th International Workshop on Mining and Learning with Graphs (MLG)},

year={2017}

}

title={Cluster Hire in a Network of Experts},

author={Meet Patel and Mehdi Kargar},

booktitle={Proceedings of the 13th International Workshop on Mining and Learning with Graphs (MLG)},

year={2017}

}

**On Generalizing Neural Node Embedding Methods to Multi-Network Problems**
PDF

*Mark Heimann and Danai Koutra*

@inproceedings{mlg2017_26,

title={On Generalizing Neural Node Embedding Methods to Multi-Network Problems},

author={Mark Heimann and Danai Koutra},

booktitle={Proceedings of the 13th International Workshop on Mining and Learning with Graphs (MLG)},

year={2017}

}

title={On Generalizing Neural Node Embedding Methods to Multi-Network Problems},

author={Mark Heimann and Danai Koutra},

booktitle={Proceedings of the 13th International Workshop on Mining and Learning with Graphs (MLG)},

year={2017}

}

**DeepInfer: Diffusion Network Inference through Representation Learning**
PDF

*Zekarias Kefato, Nasrullah Sheikh and Alberto Montresor*

@inproceedings{mlg2017_5,

title={DeepInfer: Diffusion Network Inference through Representation Learning},

author={Zekarias Kefato, Nasrullah Sheikh and Alberto Montresor},

booktitle={Proceedings of the 13th International Workshop on Mining and Learning with Graphs (MLG)},

year={2017}

}

title={DeepInfer: Diffusion Network Inference through Representation Learning},

author={Zekarias Kefato, Nasrullah Sheikh and Alberto Montresor},

booktitle={Proceedings of the 13th International Workshop on Mining and Learning with Graphs (MLG)},

year={2017}

}

**Exploiting Gaussian Embeddings for Directed Link Prediction**
PDF

*Inzamam Rahaman and Patrick Hosein*

@inproceedings{mlg2017_9,

title={Exploiting Gaussian Embeddings for Directed Link Prediction},

author={Inzamam Rahaman and Patrick Hosein},

booktitle={Proceedings of the 13th International Workshop on Mining and Learning with Graphs (MLG)},

year={2017}

}

title={Exploiting Gaussian Embeddings for Directed Link Prediction},

author={Inzamam Rahaman and Patrick Hosein},

booktitle={Proceedings of the 13th International Workshop on Mining and Learning with Graphs (MLG)},

year={2017}

}

**From Relational Data to Graphs: Inferring Significant Links using Generalized Hypergeometric Ensembles**
PDF

*Giona Casiraghi, Vahan Nanumyan, Ingo Scholtes and Frank Schweitzer*

@inproceedings{mlg2017_11,

title={From Relational Data to Graphs: Inferring Significant Links using Generalized Hypergeometric Ensembles},

author={Giona Casiraghi, Vahan Nanumyan, Ingo Scholtes and Frank Schweitzer},

booktitle={Proceedings of the 13th International Workshop on Mining and Learning with Graphs (MLG)},

year={2017}

}

title={From Relational Data to Graphs: Inferring Significant Links using Generalized Hypergeometric Ensembles},

author={Giona Casiraghi, Vahan Nanumyan, Ingo Scholtes and Frank Schweitzer},

booktitle={Proceedings of the 13th International Workshop on Mining and Learning with Graphs (MLG)},

year={2017}

}

**Evaluating Social Networks Using Task-Focused Network Inference**
PDF

*Ivan Brugere, Chris Kanich and Tanya Berger-Wolf*

@inproceedings{mlg2017_13,

title={Evaluating Social Networks Using Task-Focused Network Inference},

author={Ivan Brugere, Chris Kanich and Tanya Berger-Wolf},

booktitle={Proceedings of the 13th International Workshop on Mining and Learning with Graphs (MLG)},

year={2017}

}

title={Evaluating Social Networks Using Task-Focused Network Inference},

author={Ivan Brugere, Chris Kanich and Tanya Berger-Wolf},

booktitle={Proceedings of the 13th International Workshop on Mining and Learning with Graphs (MLG)},

year={2017}

}

**Using Partial Probes to Infer Network States**
PDF

*Pavan Rangudu, Bijaya Adhikari, B. Aditya Prakash and Anil Vullikanti*

@inproceedings{mlg2017_25,

title={Using Partial Probes to Infer Network States},

author={Pavan Rangudu, Bijaya Adhikari, B. Aditya Prakash and Anil Vullikanti},

booktitle={Proceedings of the 13th International Workshop on Mining and Learning with Graphs (MLG)},

year={2017}

}

title={Using Partial Probes to Infer Network States},

author={Pavan Rangudu, Bijaya Adhikari, B. Aditya Prakash and Anil Vullikanti},

booktitle={Proceedings of the 13th International Workshop on Mining and Learning with Graphs (MLG)},

year={2017}

}

**Compression of spatio-temporal networks via point-to-point process models**
PDF

*Xiaoyue Li and James Sharpnack*

@inproceedings{mlg2017_30,

title={Compression of spatio-temporal networks via point-to-point process models},

author={Xiaoyue Li and James Sharpnack},

booktitle={Proceedings of the 13th International Workshop on Mining and Learning with Graphs (MLG)},

year={2017}

}

title={Compression of spatio-temporal networks via point-to-point process models},

author={Xiaoyue Li and James Sharpnack},

booktitle={Proceedings of the 13th International Workshop on Mining and Learning with Graphs (MLG)},

year={2017}

}

**GECS: Graph Embedding Using Connection Subgraphs**
PDF

*Saba Al-Sayouri, Pravallika Devineni, Sarah Lam, Vagelis Papalexakis and Danai Koutra*

@inproceedings{mlg2017_28,

title={GECS: Graph Embedding Using Connection Subgraphs},

author={Saba Al-Sayouri, Pravallika Devineni, Sarah Lam, Vagelis Papalexakis and Danai Koutra},

booktitle={Proceedings of the 13th International Workshop on Mining and Learning with Graphs (MLG)},

year={2017}

}

title={GECS: Graph Embedding Using Connection Subgraphs},

author={Saba Al-Sayouri, Pravallika Devineni, Sarah Lam, Vagelis Papalexakis and Danai Koutra},

booktitle={Proceedings of the 13th International Workshop on Mining and Learning with Graphs (MLG)},

year={2017}

}

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 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
- Relationships between MLG and statistical relational learning or inductive logic programming

**Algorithms and methods:**- Graph mining
- Kernel methods for structured data
- Probabilistic and graphical models for structured data
- (Multi-) Relational data mining
- Methods for structured outputs
- Statistical models of graph structure
- Combinatorial graph methods
- Spectral graph methods
- Semi-supervised learning, active learning, transductive inference, and transfer learning in the context of graph

**Applications and analysis:**- Analysis of social media
- Social network analysis
- Analysis of biological networks
- Large-scale analysis and modeling

We invite the submission of regular research papers (6-8 pages) as well as position papers (2-4 pages).
We recommend papers to be formatted according to the standard double-column ACM Proceedings Style.
All papers will be peer reviewed, single-blinded.
Authors whose papers are accepted to the workshop will have the opportunity to participate in a poster session, and some set may also be chosen for oral presentation.
The accepted papers will be published online and will not be considered archival.

For paper submission, please proceed to the submission website.

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.

**Paper Submission Open:** ~~Apr 20, 2017~~

**Paper Submission Deadline:** ~~June 2, 2017~~

**Author Notification:** ~~June 23, 2017~~

**Camera Ready:** ~~July 7, 2017~~

**Workshop:** August 14, 2017

Stanford University

University of Maryland

University of Michigan

Google Research

UC San Diego

Purdue University

Nesreen Ahmed (Intel Labs)

Leman Akoglu (Carnegie Mellon University)

Aris Anagnostopoulos (Sapienza University of Rome)

Miguel Araujo (Carnegie Mellon University)

Stephen Bach (Stanford University)

Christian Bauckhage (Fraunhofer IAIS)

Aaron Clauset (University of Colorado Boulder)

Bing Tian Dai (Singapore Management University)

Alessandro Epasto (Google Research)

Bailey Fosdick (Colorado State University)

Brian Gallagher (Lawrence Livermore National Labs)

Thomas Gärtner (University of Nottingham)

Assefaw Gebremedhin (Washington State University)

David Gleich (Purdue University)

Larry Holder (Washington State University)

Kristian Kersting (TU Dortmund University)

Srijan Kumar (University of Maryland)

Evangelos Papalexakis (University of California Riverside)

Ali Pinar (Sandia National Laboratories)

Bryan Perozzi (Google Research)

Aditya Prakash (Virginia Tech)

Jay Pujara (University of California, Santa Cruz)

Jan Ramon (INRIA)

C. Seshadhri (University of California, Santa Cruz)

Neil Shah (Carnegie Mellon University)

Sucheta Soundarajan (Syracuse University)

Yizhou Sun (University of California, Los Angeles)

Jiliang Tang (Michigan State University)

Hanghang Tong (Arizona State University)

Chris Volinsky (AT&T Labs-Research)

Tim Weninger (University of Notre Dame)

Jevin West (University of Washington)

Stefan Wrobel (Fraunhofer IAIS & Univ. of Bonn)

Mark Zhang (SUNY, Binghamton)

2016, San Francisco, USA (co-located with KDD)

2013, Chicago, USA (co-located with KDD)

2012, Edinburgh, Scotland (co-located with ICML)

2011, San Diego, USA (co-located with KDD)

2010, Washington, USA (co-located with KDD)

2009, Leuven, Belgium (co-located with SRL and ILP)

2008, Helsinki, Finland (co-located with ICML)

2007, Firenze, Italy

2006, Berlin, German (co-located with ECML and PKDD)

2005, Porto, Portugal, October 7, 2005

2004, Pisa, Italy, September 24, 2004

2003, Cavtat-Dubrovnik, Croatia