Deadline Extended: Mining and Learning with Graphs (MLG 2011) @KDD 2011

<< By popular demand, the MLG submission deadline has been extended to May
11, 2011. >>

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Call for Papers

Ninth Workshop on *Mining and Learning with Graphs* (MLG 2011)

http://www.cs.purdue.edu/mlg2011


Held in conjunction with
       ACM Conference on Knowledge Discovery and Data Mining (KDD-2011)
       August 20-21, 2011, San Diego, California, USA


Papers due: May 11, 2011
 Acceptance notification:  June 10, 2011

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There is a growing need and interest in analyzing data that is best
represented as a graph, such as the World Wide Web, social networks, social
media, biological networks, communication networks, and physical network
systems.  Traditionally, methods for mining and learning with such graphs
has been studied independently in several research areas, including machine
learning, statistics, data mining, information retrieval, natural language
processing, computational biology, statistical physics, and sociology.
However, we note that contributions developed in one area can, and should,
impact work in the other areas and disciplines. One goal of this workshop is
to foster this type of interdisciplinary exchange, by encouraging
abstraction of the underlying problem (and solution) characteristics during
presentation and discussion.


To reflect the broad scope of work on mining and learning with graphs, we
encourage submissions that span the spectrum from theoretical analysis of
methods, 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

·         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 graphs

 *Applications and analysis:*

·         Analysis of social media

·         Social network analysis

·         Analysis of biological networks

·         Large-scale analysis and modeling


*Invited Speakers
*Lada Adamic, University of Michigan
Karsten Borgwardt, Max Planck Institute
William Cohen, Carnegie Melon University
Michelle Girvan, University of Maryland
Alon Halevy, Google Inc.
Peter Hoff, Univeristy of Washington
Michael Mahoney, Stanford University



*Program Committee
*Edoardo M. Airoldi, Harvard University

Mohammad Al Hasan, Indiana University-Purdue University Indianapolis

Aris Anagnostopoulos, Sapienza University of Rome

Arindam Banerjee, University of Minnesota

Christian Bauckhage, Fraunhofer IAIS

Francesco Bonchi, Yahoo! Research

Karsten Borgwardt, Max Planck Institute

Ulf Brefeld, Yahoo! Research

Diane Cook, Washington State University
Corinna Cortes, Google Research

Luc De Raedt, Katholieke Universiteit Leuven

Tina Eliassi-Rad, Rutgers University

Stephen Fienberg, Carnegie Melon University

Peter Flach, University of Bristol

Thomas Gartner, University of Bonn and Fraunhofer IAIS

Brian Gallagher, Lawrence Livermore National Labs

Aris Gionis, Yahoo! Research

David Gleich, Sandia National Labs

Marco Gori, University of Siena

Marko Grobelnik, J. Stefan Institute

Jiawei Han, University of Illinois at Urbana-Champaign

Shawndra Hill, University of Pennsylvania

Larry Holder, Washington State University

Jake Hofman, Yahoo! Research

Manfred Jaeger, Aalborg University

Thorsten Joachims, Cornell University

Tamara Kolda, Sandia National Labs

Jure Leskovec, Stanford University

Bo Long, Yahoo! Research

Sofus Macskassy, Fetch Technologies

Dunja Mladenic, J. Stefan Institute

Srinivasan Parthasarathy, Ohio State University

Volker Tresp, Siemens CT

Chris Volinsky, AT&T Labs Research

Stefan Wrobel, University of Bonn and Fraunhofer IAIS

Xifeng Yan, University of California at Santa Barbara

Philip Yu, University of Illinois at Chicago

Mohammed Zaki, Rensselaer Polytechnic Institute

Zhongfei (Mark) Zhang, Binghamton University



*Workshop Organizers
*Kristian Kersting, Fraunhofer IAIS and University of Bonn (
kristian.kersting@iais.fraunhofer.de)
Prem Melville, IBM Research (pmelvil@us.ibm.com)
Jennifer Neville, Purdue University (neville@cs.purdue.edu)
C. David Page Jr., University of Wisconsin Medical School (
page@biostat.wisc.edu)