<< 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 ([log in to unmask])
Prem Melville, IBM Research ([log in to unmask])
Jennifer Neville, Purdue University ([log in to unmask])
C. David Page Jr., University of Wisconsin Medical School ([log in to unmask])