- From: Prem Melville <prem.melville@gmail.com>
- Date: Sun, 17 Jul 2011 14:45:38 -0400
- To: undisclosed-recipients:;
- Message-ID: <CAFdEJKWHAusM5KJxdVYqRW_JyBtQp6ZJDGJ5eO7HRPkHcnjB-A@mail.gmail.com>
Call for Participation ======================= Ninth Workshop on Mining and Learning with Graphs 2011 (MLG-2011) http://www.cs.purdue.edu/mlg2011/index.html San Diego, CA, USA, August, 20-21, 2011 (co-located with KDD 2011) *** Register before July 31st for discounted rates *** This year's workshop on Mining and Learning with Graphs will be held in conjunction with the 17th ACM SIGKDD Conference on Knowledge Discovery and Data Mining that will take place in August 21-24, 2011, in San Deigo, CA, USA. 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. To reflect the broad scope of work on mining and learning with graphs, we encouraged 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 encouraged submissions on theory, methods, and applications focusing on the analysis of social media. Registration ============ You can register via http://www.kdd.org/kdd2011/registration.shtml Program ======= Both days will consist of keynote speakers and short presentations showcasing accepted papers. To promote dialog we will have a panel discussion and a poster session. The program features the following invited talks and accepted papers: Invited Talks ------------- * Lada Adamic, University of Michigan * Karsten Borgwardt, Max Planck Institute * William Cohen, Carnegie Melon University * Charles Elkan, University of California, San Diego * Michelle Girvan, University of Maryland * Alon Halevy, Google Inc. * Peter Hoff, Univeristy of Washington * Michael Mahoney, Stanford University Accepted Papers --------------- * Comparing Generalizations of Unweighted Network Measures Sherief Abdallah and Habab Musa * A Framework for Hypothesis Learning Over Sets of Vectors Karim Abou-Moustafa and Frank Ferrie * All-at-once Optimization for Coupled Matrix and Tensor Factorizations Evrim Acar, Tamara Kolda and Daniel Dunlavy * Evaluating Markov Logic Networks for Collective Classification Robert Crane and Luke Mcdowell * Graph Based Statistical Analysis of Network Traffic Hristo Djidjev, Gary Sandine, Curtis Storlie and Scott Vander Wiel * Compression versus Frequency for Mining Patterns and Anomalies in Graphs William Eberle and Lawrence Holder * Learning from Plane Graphs Thomas Fannes and Jan Ramon * Bayesian Optimal Active Search on Graphs Roman Garnett, Yamuna Krishnamurthy, Donghan Wang, Jeff Schneider and Richard Mann * Can’t See Forest through the Trees? Understanding Mixed Network Traffic Graphs from Application Class Distribution Yu Jin, Nick Duffield, Patrick Haffner, Subhabrata Sen and Zhi-Li Zhang * Graph Classification via Topological and Label Attributes Geng Li, Murat Semerci, Bülent Yener and Mohammed Zaki * Adaptation of Graph-Based Semi-Supervised Methods to Large-Scale Text Data Frank Lin and William W. Cohen * Building a Semantic Graph based on Sequential Language Model for Topic-Sensitive Content Extraction Yan Liang and Ying Liu * Small Network Segmentation with Template Guidance Kristin Lui and Ian Davidson * Generating Similar Graphs from Spherical Features Dalton Lunga and Sergey Kirshner * Predicting Dynamic Difficulty Olana Missura and Thomas Gaertner * Mining Differential Hubs in Homogenous Networks Omar Odibat and Chandan K. Reddy * Integrating Logic Knowledge into Graph Regularization: an application to image tagging Claudio Sacca, Michelangelo Diligenti, Marco Gori and Marco Maggini * Exploring Co-Occurrence on a Meso and Global Level Using Network Analysis and Rule Mining Maximilian Schich and Michele Coscia * Transductive Classification Methods for Mixed Graphs Sundararajan Sellamanickam and Sathiya Keerthi * Graph Mining of Motif Profiles For Computer Network Activity Inference William Turkett, Errin Fulp, Charles Lever and Edward Allan * Trading off Propagation and Misspecification Error to Reduce Inference Error in Probabilistic Relational Models Rongjing Xiang and Jennifer Neville * Visualizing the Temporal Evolution of Dynamic Networks Kevin Xu, Mark Kliger and Alfred Hero We look forward to seeing you in San Diego! Kristian Kersting, Fraunhofer IAIS and University of Bonn Prem Melville, IBM Research Jennifer Neville, Purdue University C. David Page Jr., University of Wisconsin Medical School
Received on Sunday, 17 July 2011 18:46:06 UTC