As a member of the program committee for the workshop Statistical Network Analysis: Models, Issues and New Directions, part of the 2006 International Conference on Machine Learning, I urge you to submit your best work by 28 April; you can send us the bad stuff after that. (That, in response to hearing endless variants on "First prize, a trip to Pittsburgh; second prize, two trips", which was old in my grandfather's day.)
CALL FOR PAPERS
Statistical Network Analysis:a workshop at the
Models, Issues and New Directions
23rd International Conference on Machine LearningThursday, June 29, 2006, Pittsburgh PA, USA
This workshop focuses on probabilistic methods for network analysis, paying special attention to model design and computational issues of learning and inference.
Many modern data analysis problems involve large data sets of artificial, social, and biological networks. In these settings, traditional IID assumptions are blatantly inappropriate; the analyses must take into account the structure of relationships between the data. As a result, there has been increasing research developing techniques for incorporating network structures into machine learning and statistics.
Network modeling is an active area of research in several domains. Statisticians have mostly concentrated on models of static networks. These models are concerned with the existence of edges between individual nodes, but do not attempt to model aggregate properties. In contrast, physicists have addressed global properties of large complex networks. Their models describe average statistics of the network, or properties of typical networks in large ensembles; the links between particular nodes are less meaningful.
This workshop aims to bring together statistical network modeling researchers from different communities, thereby fostering collaborations and intellectual exchange. Our hope is that this will result in novel modeling approaches, diverse applications, and new research directions.
We wish to clarify that in this workshop, the word "relational" carries a different meaning from the usual sense of the word in Probabilistic Relational Models (PRMs). For example, in real life, any two random people maybe connected through a complex web of friendships; estimation of interpersonal connections thus cannot be done independently of the rest of the network. We focus on modeling statistical properties of the network, as opposed to different types of probabilistic relations. This differentiates us from the co-located ICML workshop on Statistical Relational Models.
We welcome the following types of papers:
We encourage authors to emphasize the role of learning and its relevance to the application domains at hand. In addition, we hope to identify current successes in the area, and will therefore consider papers that apply previously proposed models to novel domains and data sets.
- research papers that introduce new models or apply established models to novel domains,
- research papers that explore theoretical and computational issues,
- position papers that discuss shortcomings and desiderata of current approaches, or propose new directions for future research.
Submissions should be limited to a maximum of 8 pages, and adhere to ICML format. Please email your submissions to: edo [at] cmu.edu.
Deadline for Submissions: Friday, April 28, 2006
Notification of Decision: Friday, May 5, 2006
Format:This is a one-day workshop. It will consist of several themed sessions targeting methodological and application issues (e.g., estimation in static models, network evolution modeling, and statistical modeling of large scale networks) with talks (invited and contributed) and moderated discussion. Discussions at the workshop will facilitate exchanging of research ideas and help identify other challenging problems in the area. At the end of the workshop, a panel of statisticians, physicists, and computer scientists will discuss the points arising throughout the day and identify the most promising and challenging directions.
Publication:Accepted papers will be distributed on a CD and made available for download.
Organizers:Edo Airoldi, Carnegie Mellon University
David Blei, Princeton University
Stephen Fienberg, Carnegie Mellon University
Anna Goldenberg, Carnegie Mellon University
Eric Xing, Carnegie Mellon University
Alice Zheng, Carnegie Mellon University
Program Committee:David Banks, Duke University
Peter Dodds, Columbia University
Lise Getoor, University of Maryland
Mark Handcock, University of Washington, Seattle
Peter Hoff, University of Washington, Seattle
David Jensen, University of Massachusetts, Amherst
Alan Karr, National Institute of Statistical Sciences
Jon Kleinberg, Cornell University
Andrew McCallum, University of Massachusetts, Amherst
Foster Provost, New York University
Cosma Shalizi, Carnegie Mellon University
Padhraic Smyth, University of California, Irvine
Josh Tenenbaum, Massachusetts Institute of Technology
Stanley Wasserman, Indiana University
Posted at March 18, 2006 15:06 | permanent link