Identifying Important Nodes in Heterogenous Networks

This is a position paper that presents a new approach to identifying important nodes or entities in a complex heterogeneous network. We provide a novel definition of an importance score based on a statistical model: An individual is important to the extent that including an individual explicitly in the model improves the data fit of the model more than it increases the model's complexity. We apply techniques from statistical-relational learning, a recent field that combines AI and machine learning, to identify statistically important individuals in a scalable manner. We investigate empirically our approach with the OPTA soccer data set for the English premier league.