Connectionists: [CFP] KDD MLG 2017 - 13th International Workshop on Mining and Learning with Graphs

[Shobeir Fakhraei]

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/
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 chair at mlgworkshop.org

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)
Julian McAuley (UC San Diego)
Jennifer Neville (Purdue University)

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
or follow the twitter account: https://twitter.com/mlgworkshop

Best Regards,
-- MLG Organizers
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