Format

Send to

Choose Destination
KDD. 2017 Aug;2017:425-434. doi: 10.1145/3097983.3097990.

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

Author information

1
University of Illinois at Urbana-Champaign.

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.

KEYWORDS:

Heterogeneous information networks; graph mining; meta-paths; relevance measures

Supplemental Content

Full text links

Icon for PubMed Central
Loading ...
Support Center