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.

Traditionally, a number of subareas have worked with mining and learning from graph structured data, including communities in graph mining, learning from structured data, statistical relational learning, inductive logic programming, and, moving beyond subdisciplines in computer science, social network analysis, and, more broadly network science.

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.

Organization Committee

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