Connectionists: CFP: Eighth International Workshop on Mining and Learning from Graphs, MLG 2010 @ KDD 2010

[Lise Getoor]

Call for Papers
Eighth Workshop on Mining and Learning with Graphs  2010 (MLG-2010)
http://www.cs.umd.edu/mlg2010
Washington, DC, July, 24-25
(co-located with KDD 2010 <http://www.sigkdd.org/kdd2010>)
Submission Deadline May 7, 2010


This year's workshop on Mining and Learning with Graphs will be held in 
conjunction with the 16th ACM SIGKDD Conference on Knowledge Discovery 
and Data Mining that will take place in July 25-28, 2010 in Washington, DC.

There is a great deal of interest in analyzing data that is best 
represented as a graph. Examples include the WWW, social networks, 
biological networks, communication networks, and many others. The 
importance of being able to effectively mine and learn from such data is 
growing, as more and more structured and semi-structured data is 
becoming available.  This is a problem across widely different fields 
such as biology, economics, statistics, social science, physics and 
computer science, and is studied within a variety of sub-disciplines of 
machine learning and data mining including graph mining, graphical 
models, kernel theory, statistical relational learning, etc.

The objective of this workshop is to bring together researchers from a 
variety of these areas, and discuss commonality and differences in 
challenges faced, survey some of the different approaches, and provide a 
forum to present and learn about some of the most cutting edge research 
in this area.  As an outcome, we expect participants to walk away with a 
better sense of the variety of different tools available for graph 
mining and learning, and an appreciation for some of the interesting 
emerging applications for mining and learning from graphs. The key 
challenge we address in this workshop is how to efficiently analyze 
large data sets that are relational in nature and hence easily 
represented as graphs.  Such data are becoming ubiquitous in a plethora 
of application and research domains and now is the time to bring 
together people from these various fields to exchange ideas about how we 
can mine and learn from these large data sets. The goal of this workshop 
will be to explore the state-of-the-art algorithms and methods, 
leveraging existing knowledge from other sub-disciplines, to examine 
graph-based models in the context of real-world applications, and to 
identify future challenges and issues. In particular we are interested 
in the following topics:

   * Graph mining
   * Kernel methods for structured data
   * Probabilistic models for structured data
   * (Multi-)relational data mining
   * Methods for structured outputs
   * Network analysis
   * Large-scale learning and applications
   * Sampling issues in graph algorithms
   * Evaluation of graph algorithms
   * Relationships between mining and learning with graphs and
     statistical relational learning
   * Relationships between mining and learning with graphs and
     inductive logic programming
   * Semi-supervised learning
   * Active learning
   * Transductive inference
   * Transfer learning


We invite researchers working on mining and learning with graphs to 
submit regular and position papers detailing the major points and/or 
results they would present during a talk. Regular papers are a maximum 
of 8 pages long in two-column format, position papers comprise 2 pages. 
Authors whose papers were accepted to the workshop will have the 
opportunity to give a short presentation at the workshop as well as 
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, 
discussions at end of sessions, and a poster session to promote dialog

Important Dates

Paper submission deadline        May  7
Notification of acceptance        May 21
Final paper deadline                 May 28
Workshop                               July 24-25

Organizers

*  Ulf Brefeld (Yahoo! Research, Barcelona)
*  Lise Getoor (University of Maryland)
*  Sofus A. Macskassy (Fetch Technologies / USC)

Program Committee

   * Edo Arioldi
   * Tanya Berger-Wolf
   * Hendrik Blockeel
   * Karsten Borgwardt
   * Chris Burges
   * Diane Cook
   * Tina Eliassi-Rad
   * Stephen Fienberg
   * Paolo Frasconi
   * Thomas Gaertner
   * Brian Gallagher
   * Aris Gionis
   * Marko Grobelnik
   * Jiawei Han
   * Susanne Hoche
   * Lawrence Holder
   * Jure Leskovec
   * George Karypis
   * Samuel Kaski
   * Kristian Kersting
   * Dunja Mladenic
   * Alessandro Moschitti
   * Jennifer Neville
   * Massimiliano Pontil
   * Foster Provost
   * Padhraic Smyth
   * Swapna Somasundaran
   * Eric Xing
   * Philip Yu
   * Mohammed Zaki
   * Fabio Massimo Zanzotto
   * Zhongfei (Mark) Zhang


Contact Information

*  Sofus Macskassy  
  Fetch Technologies
  841 Apollo Street
  Suite 400
  El Segundo, CA 90245
  http://www.cs.rutgers.edu/~sofmac
  http://www-rcf.usc.edu/~macskass
  sofmac at fetch.com

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