This year's Workshop on Mining and Learning with Graphs will be held in conjunction with the 17th ACM SIGKDD Conference on Knowledge Discovery and Data Mining that will take place August 21-24, 2011 in San Diego, CA.
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.