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CALL FOR PAPERS
Analyzing Networks and Learning with Graphs
a workshop in conjunction with
23nd Annual Conference on Neural Information Processing Systems
(NIPS 2009)
December 11, 2009, Whistler, BC, Canada
http://snap.stanford.edu/nipsgraphs2009/
Deadline for Submissions: Friday, November 4, 2009 **updated**
Notification of Decision: Monday, November 9, 2009
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Overview:
Recent research in machine learning and statistics has seen the
proliferation of computational methods for analyzing networks and
learning with graphs. These methods support progress in many
application areas, including the social sciences, biology, medicine,
neuroscience, physics, finance, and economics.
The primary goal of the workshop is to actively promote a concerted
effort to address statistical, methodological and computational issues
that arise when modeling and analyzing large collection of data that
are largely represented as static and/or dynamic graphs. To this end,
we aim at bringing together researchers from applied disciplines such
as sociology, economics, medicine and biology, together with
researchers from more theoretical disciplines such as mathematics and
physics, within our community of statisticians and computer
scientists. Different communities use diverse ideas and mathematical
tools; our goal is to to foster cross-disciplinary collaborations and
intellectual exchange.
Presentations will include novel graph models, the application of
established models to new domains, theoretical and computational
issues, limitations of current graph methods and directions for future
research.
Online Submissions:
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We welcome the following types of papers:
1. Research papers that introduce new models or apply established
models to novel domains,
2. Research papers that explore theoretical and computational issues, or
3. Position papers that discuss shortcomings and desiderata of current
approaches, or propose new directions for future research.
All submissions will be peer-reviewed; exceptional work will be
considered for oral presentation. 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 4-to-8 pages long, and adhere to NIPS format
(http://nips.cc/PaperInformation/StyleFiles). Please email your
submissions to: nipsgraphs2...@gmail.com
Deadline for Submissions: Friday, November 4, 2009, 11:59 pm (EST)
Notification of Decision: Friday, November 6 2009
Workshop Format:
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This is a one-day workshop. The program will feature invited talks,
poster sessions, poster spotlights, and a panel discussion. All
submissions will be peer-reviewed; exceptional work will be considered
for oral presentation. Details about the program will be announced in
late November. Invited speakers include: Jennifer Chayes (Microsoft),
Matthew Jackson (Stanford), Ravi Kumar (Yahoo), Martina Morris (UW),
Cosma Shalizi (CMU).
Organizers:
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Edo Airoldi, Harvard University
Jure Leskovec, Stanford University
Jon Kleinberg, Cornell University
Josh Tenenbaum, MIT
Thank you, we look forward to receiving your submissions!
Edo, Jure, Jon & Josh
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