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*13th International Workshop on Mining and Learning with Graphs (MLG 2017)*
*August 14, 2017 - Halifax, Nova Scotia, Canada (co-located with KDD 2017)*
*http://www.mlgworkshop.org/2017 <http://www.mlgworkshop.org/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:

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
* 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.*


*Timeline:*
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*
http://www.mlgworkshop.org/2017/

Please send enquiries to *[log in to unmask] <[log in to unmask]>*

We look forward to seeing you at the 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)

To receive updates about the current and future workshops and the Graph
Mining community, please join the mailing list:
*https://groups.google.com/d/forum/mlg-list
<https://groups.google.com/d/forum/mlg-list>   *
or follow the twitter account: *https://twitter.com/mlgworkshop
<https://twitter.com/mlgworkshop>*

Best Regards,
-- MLG Organizers

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