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MLG 2013 : Eleventh Workshop on Mining and Learning with Graphs

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Conference Series : Mining and Learning with Graphs
 
Link: http://snap.stanford.edu/mlg2013
 
When Aug 11, 2013 - Aug 11, 2013
Where Chicago, USA
Submission Deadline Jun 6, 2013
Categories    data mining
 

Call For Papers

Eleventh Workshop on Mining and Learning with Graphs (MLG 2013)
August 11, 2013 - Chicago, IL (co-located with KDD 2013)
http://snap.stanford.edu/mlg2013/
Submission Deadline: June 6, 2013

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. In terms
of application areas, 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. Social media
analytics is a fertile ground for research at the intersection of mining graphs
and text. As such, this year we especially encourage submissions on theory,
methods, and applications focusing on the analysis of social media.

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). 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.

Timeline:
Submission Deadline: June 6
Notification: June 25
Final Version: July 6
Workshop: August 11

Submission instructions can be found on
http://snap.stanford.edu/mlg2013/instructions.html

Please send enquires to jmcauley@cs.stanford.edu

We look forward to seeing you at the workshop!

Lada Adamic, Lise Getoor, Bert Huang, Jure Leskovec, Julian McAuley (chairs)

Edoardo Airoldi, Leman Akoglu, Aris Anagnostopoulos, Arindam Banerjee, Christian
Bauckhage, Francesco Bonchi, Ulf Brefeld, Thomas Gaerner, Brian Gallagher, David
Gleich, Marco Gori, Mohammad Hasan, Jake Hofman, Jiawei Han, Larry Holder,
Manfred Jaeger, Tamara Kolda, U Kang, Kristian Kersting, Kristina Lerman, Bo
Long, Sofus Macskassy, Prem Melville, Dunja Mladenic, Jennifer Neville,
Srinivasan Parthasarathy, Jan Ramon, Bing Tian Dai, Hanghang Tong, Chris
Volinsky, Xifeng Yan, Mohammed Zaki, Liang Zhang, Mark Zhang (program committee)

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