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Ninth Workshop on Mining and Learning with Graphs (MLG 2011)

From: Prem Melville <prem.melville@gmail.com>
Date: Fri, 1 Apr 2011 18:56:47 -0400
Message-ID: <AANLkTikKSEBWA1vFq3hYyW7_+wvwJtTTMsr6mSfj7suX@mail.gmail.com>
To: undisclosed-recipients:;
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Call for Papers

Ninth Workshop on Mining and Learning with Graphs (MLG 2011)

http://www.cs.purdue.edu/mlg2011

Held in conjunction with
 ACM Conference on Knowledge Discovery and Data Mining (KDD-2011)
 August 20-21, 2011, San Diego, California, USA

Papers due: May 6, 2011

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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.

To reflect the broad scope of work on mining and learning with graphs,
we encourage 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 encourage
submissions on theory, methods, and applications focusing on the
analysis of social media.

Topics of interest include, but are not limited to:

Theoretical aspects:

 Computational or statistical learning theory related to graphs

 Theoretical analysis of graph algorithms or models

 Sampling and evaluation issues in graph algorithms

 Relationships between MLG and statistical relational
learning or inductive logic programming

Algorithms and methods:

 Graph mining

 Kernel methods for structured data

 Probabilistic and graphical models for structured data

 (Multi-) Relational data mining

 Methods for structured outputs

 Statistical models of graph structure

 Combinatorial graph methods

 Spectral graph methods

 Semi-supervised learning, active learning, transductive
inference, and transfer learning in the context of graphs

Applications and analysis:

 Analysis of social media

 Social network analysis

 Analysis of biological networks

 Large-scale analysis and modeling

We invite the submission of regular research papers as well as
position papers. Authors whose papers are accepted to the workshop
will have the opportunity to give a short presentation at the workshop
and/or present their work in a poster session to promote interaction
and dialog.

The workshop itself is a two-day workshop. Each day will consist of
keynote speakers, short presentations showcasing accepted papers, and
a poster session to promote dialogue.

Workshop Organizers
==================
Kristian Kersting, Fraunhofer IAIS and University of Bonn
(kristian.kersting@iais.fraunhofer.de)
Prem Melville, IBM Research (pmelvil@us.ibm.com)
Jennifer Neville, Purdue University (neville@cs.purdue.edu)
C. David Page Jr., University of Wisconsin Medical School
(page@biostat.wisc.edu)
Received on Monday, 4 April 2011 17:01:42 GMT

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