%0 Journal Article
%J SIGKDD Explor. Newsl.
%D 2011
%T Eighth workshop on mining and learning with graphs
%A Brefeld,Ulf
%A Getoor, Lise
%A Macskassy,Sofus A.
%K data mining
%K dynamic network analysis
%K graph mining
%K kernel methods
%K link mining
%K machine learning
%K network analysis
%K pattern recognition
%K relational learning
%K scalable graph mining
%K statistical relational learning
%X The Eighth Workshop on Mining and Learning with Graphs (MLG)1was held at KDD 2010 in Washington DC. It brought together a variete of researchers interested 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 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 was to bring together practitioners from these various fields and areas to foster a rich discussion of which problems we work on, how we frame them in the context of graphs, which tools and algorithms we apply and our general findings and lessons learned. This year's workshop was very successful with well over 100 attendees, excellent keynote speakers and papers. This is a rapidly growing area and we believe that this community is only in its infancy. We hope that the readers will join us next year for MLG 2011.
%B SIGKDD Explor. Newsl.
%V 12
%P 63 - 65
%8 2011/03//
%@ 1931-0145
%G eng
%U http://doi.acm.org/10.1145/1964897.1964915
%N 2
%R 10.1145/1964897.1964915