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, transportation networks, energy grids, and many others. These graphs are typically multi-modal, multi-relational and dynamic. In the era of big data, 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. The workshop serves as a forum for researchers from a variety of fields working on mining and learning from graphs to share and discuss their latest findings.

There are many challenges involved in effectively mining and learning from this kind of data, including:

• Understanding the different techniques applicable, including graph mining algorithms, graphical models, latent variable models, matrix factorization methods and more.
• Dealing with the heterogeneity of the data.
• The common need for information integration and alignment.
• Handling dynamic and changing data.
• Addressing each of these issues at scale.

Traditionally, a number of subareas have contributed to this space: 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.

More information available at the MGL 2013 workshop website.