  As a powerful representation paradigm for networked and multi-typed data, the
heterogeneous information network (HIN) is ubiquitous. Meanwhile, defining
proper relevance measures has always been a fundamental problem and of great
pragmatic importance for network mining tasks. Inspired by our probabilistic
interpretation of existing path-based relevance measures, we propose to study
HIN relevance from a probabilistic perspective. We also identify, from
real-world data, and propose to model cross-meta-path synergy, which is a
characteristic important for defining path-based HIN relevance and has not been
modeled by existing methods. A generative model is established to derive a
novel path-based relevance measure, which is data-driven and tailored for each
HIN. We develop an inference algorithm to find the maximum a posteriori (MAP)
estimate of the model parameters, which entails non-trivial tricks. Experiments
on two real-world datasets demonstrate the effectiveness of the proposed model
and relevance measure.

Social and Information Networks;Machine Learning