Held in conjunction with KDD'17
Aug 14, 2017 - Halifax, Nova Scotia, Canada
13th 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, 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.

Call for Papers

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.

Important Dates

 

Paper Submission Open: Apr 20, 2017

Paper Submission Deadline: May 26, 2017 June 2, 2017

Author Notification: June 16, 2017

Final Version: June 25, 2017

Workshop: August 14, 2017

Workshop Organizers

Michele Catasta

EPFL / Stanford

Shobeir Fakhraei

University of Maryland

Danai Koutra

University of Michigan

Silvio Lattanzi

Google Research

Julian McAuley

UC San Diego

Jennifer Neville

Purdue University

Program Committee

 

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)

 

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