Probabilistic interpretation of path-based relevance in heterogeneous information networks

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 the 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.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2017

Author dc:creator

• Chan, Po-Wei

Contributors dc:contributor

• Han, Jiawei

• Relevance measure
• Heterogeneous information network
• Generative model

Statement dc:rights

• Copyright 2017 Po-Wei Chan

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/97416

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/97416