Call for Papers: Mining and Learning with Graphs (MLG 2011) @KDD 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 6, 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:
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*Theoretical aspects:*
ƒL Computational or statistical learning theory related to graphs
ƒL Theoretical analysis of graph algorithms or models
ƒL Sampling and evaluation issues in graph algorithms
ƒL Relationships between MLG and statistical relational learning or
inductive logic programming
*Algorithms and methods:*
ƒL Graph mining
ƒL Kernel methods for structured data
ƒL Probabilistic and graphical models for structured data
ƒL (Multi-) Relational data mining
ƒL Methods for structured outputs
ƒL Statistical models of graph structure
ƒL Combinatorial graph methods
ƒL Spectral graph methods
ƒL Semi-supervised learning, active learning, transductive inference,
and transfer learning in the context of graphs
 *Applications and analysis:*
ƒL Analysis of social media
ƒL Social network analysis
ƒL Analysis of biological networks
ƒL 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
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)
*