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Title: PReP: Path-Based Relevance from a Probabilistic Perspective in Heterogeneous Information Networks
As a powerful representation paradigm for networked and multityped 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.  more » « less
Award ID(s):
1704532 1618481
PAR ID:
10059907
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
Proceedings of the 23rd {ACM} {SIGKDD} International Conference on Knowledge Discovery and Data Mining
Volume:
23
Issue:
1
Page Range / eLocation ID:
425 to 434
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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