Held in conjunction with KDD'20
Aug 24, 2020 - San Diego, CA, USA
16th International Workshop on
Mining and Learning with Graphs
Call for Papers

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.

Keynote Speakers

Keynotes will be announced soon!

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

Research 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

Siddharth Bhatia (National University of Singapore)
Jundong Li (University of Virginia)
Shin-Chieng Ngo (Xin-Zeng Wu) (USC-ISI)
Stefano Leucci (University of L'Aquila)
Jin Kyu Kim (Facebook)
Hocine Cherifi (University of Burgundy)
Dhivya Eswaran (Amazon)
Ting Chen (University of California, Los Angeles)
Ivan Brugere (University of Illinois at Chicago)
Yuan Fang (Singapore Management University)
Blaz Novak (Jozef Stefan Institute)
Sucheta Soundarajan (Syracuse University)
Fred Morstatter (University of Southern California)
Acar Tamersoy (NortonLifeLock Research Group)
John Palowitch (Google)
Austin Benson (Cornell University)

Hanghang Tong (University of Illinois at Urbana-Champaign)
Larry Holder (Washington State University)
Aaron Clauset (University of Colorado Boulder)
Jan Ramon (INRIA)
Christian Bauckhage (Fraunhofer)
Bryan Hooi (National University of Singapore)
William Hamilton (Stanford University)
Aris Anagnostopoulos (Sapienza University of Rome)
Ulf Brefeld (Leuphana Universität Lüneburg)
Ali Pinar (Sandia National Laboratories)
Alessandro Epasto (Google)
Danai Koutra (University of Michigan)
Evangelos Papalexakis (University of California Riverside)
Stefan Wrobel (Fraunhofer IAIS & Univ. of Bonn)
Ana Paula Appel (IBM Research Brazil)
Marco Bressan (Sapienza University of Rome)

Previous Workshops