Topics of interest include, but are not limited to:

Theoretical aspects:

• Computational or statistical learning theory related to graphs

• Theoretical analysis of graph algorithms or models

• Sampling and evaluation issues in graph algorithms

• Relationships between MLG and statistical relational learning or inductive logic programming

Algorithms and methods:

• Graph mining

• Kernel methods for structured data

• Probabilistic and graphical models for structured data

• (Multi-) Relational data mining

• Methods for structured outputs

• Statistical models of graph structure

• Combinatorial graph methods

• Spectral graph methods

• Semi-supervised learning, active learning, transductive inference, and transfer learning in the context of graph

Applications and analysis:

• Analysis of social media

• Social network analysis

• Analysis of biological networks

• Large-scale analysis and modeling