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Mining and Learning on Graphs (MLG'07)
Firenze (Italy), August 1-3 2007
http://mlg07.dsi.unifi.it
Call for Papers
Data Mining and Machine Learning are in the midst of a "structured
revolution". After many decades of focusing on independent and
identically-distributed (iid) examples, many researchers are now
studying problems in which examples consist of collections of
inter-related entities or are linked together into complex graphs. A
major driving force is the explosive growth in the amount of
heterogeneous data that is being collected in the business and
scientific world. Example domains include bioinformatics,
chemoinformatics, transportation systems, communication networks,
social network analysis, link analysis, robotics, among others.
We believe this is an ideal time for a workshop that allows active
researchers in this area to discuss and debate the unique challenges of
mining and learning from structured data. The MLG 2007 workshop will
thus concentrate on mining and learning with structured data in general
and its many appearances and facets such as interpretations, graphs,
trees, sequences. Specifically, we seek to invite researchers in
Statistical Relational Learning, Kernel Methods for Structured
Inputs/Outputs, Graph Mining, (Multi-) Relational Data Mining,
Inductive Logic Programming, among others.
The main objective of this event is to bring researchers together and
foster discussion and collaborations. For this reason we would like to
keep the workshop as open as possible. We invite the submission of
extended abstracts up to 4 pages. Since we would like to hear about
your most recent and best research results, there will be no formal
proceedings that could prevent you from submitting your contribution to
other venues. The program committee will select the best submissions
for oral and for poster presentation. A booklet of accepted abstracts
will be distributed at the workshop. Authors of selected original
papers will be encouraged to submit to a journal special issue planned
for the end of 2007.
We encourage submissions on a wide variety of facets of mining and
learning on graphs, including theoretical and algorithmic issues,
challenges and open problems, comparative and empirical studies, and
applications. The following is a non exclusive list of topics of
interest:
Kernel methods for graphs and structured data
Frequent (sub)graph mining
Probabilistic models involving sequences or graphs
Supervised, unsupervised, semisupervised, and transductive learning in
graphical domains
Supervised learning with structured outputs
Representations for mining and learning with graphs
Theoretical issues
Please submit extended abstracts up to 4 pages in pdf format by email
to mlg07@dsi.unifi.it
Important dates:
Paper submission deadline: May 10th, 2007
Notification to authors: June 10th, 2007
Workshop: August 1st-3rd, 2007
Co-Chairs:
Paolo Frasconi, Università degli Studi di Firenze, Italy.
Kristian Kersting, MIT, Cambridge, USA.
Koji Tsuda, Max Planck Institute for Biological Cybernetics, Tuebingen,
Germany.
Steering Committee:
Luc De Raedt, Katholieke Universiteit Leuven, Belgium.
Thomas Gaertner, Fraunhofer IAIS, Sankt Augustin, Germany.
Gemma Garriga, Helsinki University of Technology, Finland
George Karypis, University of Minnesota, Minneapolis, USA
Joost Kok, Universiteit Leiden, The Netherlands
Thorsten Meinl, University of Konstanz, Germany
Siegfried Nijssen, Katholieke Universiteit Leuven, Belgium
Takashi Washio, Osaka University, Japan
Invited Keynote Speakers:
Pierre Baldi, University of California at Irvine, USA.
Luc De Raedt, Katholieke Universiteit Leuven, Belgium.
Jiawei Han, Univ. of Illinois at Urbana-Champaign, USA.
Lise Getoor, University of Maryland, USA.
Alex Smola, National ICT Australia. |
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