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 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, 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:

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

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 dialogue.