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                            CALL FOR PAPERS

                     Statistical Network Analysis:
                   Models, Issues and New Directions

                           a workshop at the

           23rd International Conference on Machine Learning
                             (ICML 2006)

               Thursday, June 29, 2006, Pittsburgh PA, USA

                http://www.cs.cmu.edu/~eairoldi/nets/

           Deadline for Submissions: Friday, April 28, 2006
           Notification of Decision: Friday, May 5, 2006

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Overview:
---------
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 at bringing 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.


Online Submissions:
-------------------
We welcome the following types of papers:

* 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.

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.

Submissions should be limited to a maximum of 8 pages, and adhere to
ICML format (http://www.icml2006.org/icml2006/templates.html).
Please email your submissions to: [log in to unmask]

Deadline for Submissions: Friday, April 28, 2006
Notification of Decision: Friday, May 5, 2006

Those interested in this workshop might also be interested in the
workshop on Statistical Relational Learning (http://www.icml2006.org/
icml2006/workshops.html).  We will consider planning a joint session,
for part of the day, if there is sufficient overlap in interests.


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.

The tentative schedule format is as follows.

Morning session:
    Introductions
    Invited talks
    Submitted talks (20 min each)
    Break

Afternoon session:
    Invited talks
    Submitted talks (20 min each)
    Break
    Panel discussion


Publication:
------------
Accepted papers will be distributed on a CD and made available for
download.  We are also negotiating the publication of the accepted
papers in print form.


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


Thank you, we look forward to receiving your submissions!

Edo Airoldi, David Blei, Stephen Fienberg, Anna Goldenberg, Eric Xing
& Alice Zheng.

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