13th International Workshop on Mining and Learning with Graphs (MLG 2017)
August 14, 2017 - Halifax, Nova Scotia, Canada (co-located with KDD 2017)
Submission Deadline: May 26, 2017
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:
* 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
* 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
* Knowledge graph construction
* 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 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 spotlight and 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.
Paper Submission Deadline: May 26, 2017
Author Notification: June 16, 2017
Final Version: June 25, 2017
Workshop: August 14, 2017
Submission instructions can be found on
We look forward to seeing you at the workshop!
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)
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)
-- MLG Organizers