# PReP: Path-Based Relevance from a Probabilistic Perspective in Heterogeneous Information Networks

## TL;DR

This paper introduces PReP, a probabilistic model for measuring relevance in heterogeneous information networks, capturing cross-meta-path synergy and demonstrating improved effectiveness on real datasets.

## Contribution

It proposes a novel probabilistic framework for path-based relevance in HINs, modeling cross-meta-path synergy and providing a data-driven relevance measure.

## Key findings

- The model effectively captures relevance in real-world HINs.
- Experimental results show improved relevance measurement accuracy.
- The approach outperforms existing path-based relevance methods.

## Abstract

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.