9th International Workshop on Mining with Graphs

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.

In particular, this workshop is intended to serve as a forum for exchanging ideas and methods, 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 the related disciplines, including academia, industry and government, and create a forum for discussing recent advances in analysis of graphs. 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.

Organization Committee

• Kristian Kersting, Fraunhofer IAIS and University of Bonn
• Prem Melville, IBM Research
• Jennifer Neville, Purdue University
• David Page, University of Wisconsin

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