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:
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.
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:
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 firstname.lastname@example.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:
May 26, 2017 June 2, 2017
Author Notification: June 16, 2017
Final Version: June 25, 2017
Workshop: August 14, 2017
EPFL / Stanford
University of Maryland
University of Michigan
UC San Diego
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