PReP: Path-Based Relevance from a Probabilistic Perspective in Heterogeneous Information Networks
This work addresses a fundamental problem in network mining for researchers and practitioners, but it is incremental as it builds on existing path-based methods.
The paper tackled the problem of defining relevance measures in heterogeneous information networks by proposing a probabilistic perspective and modeling cross-meta-path synergy, resulting in a data-driven relevance measure that demonstrated effectiveness in experiments on two real-world datasets.
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.