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Perspectives on Neuroscience

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Published on: July 31, 2007

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

Yu Shi¹, Po-Wei Chan¹, Honglei Zhuang¹

¹University of Illinois at Urbana-Champaign.

KDD : Proceedings. International Conference on Knowledge Discovery & Data Mining

|September 18, 2018

Summary

This study introduces a novel probabilistic approach to measure relevance in heterogeneous information networks (HINs). It models cross-meta-path synergy for more accurate, data-driven relevance in network mining.

Keywords:

Heterogeneous information networks graph mining meta-paths relevance measures

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Area of Science:

Data Science
Network Analysis
Machine Learning

Background:

Heterogeneous Information Networks (HINs) are crucial for representing complex, multi-typed data.
Defining accurate relevance measures is fundamental for effective network mining tasks.
Existing path-based relevance measures lack the ability to model cross-meta-path synergy.

Purpose of the Study:

To propose a probabilistic perspective for understanding and measuring relevance in HINs.
To introduce a novel approach that models cross-meta-path synergy, a key characteristic of HINs.
To develop a data-driven, tailored relevance measure for specific HINs.

Main Methods:

Developed a probabilistic interpretation of existing relevance measures.
Identified and modeled cross-meta-path synergy from real-world data.
Established a generative model to derive a novel path-based relevance measure.
Designed an inference algorithm for Maximum A Posteriori (MAP) parameter estimation.

Main Results:

A novel, data-driven path-based relevance measure for HINs was derived.
The proposed model effectively captures cross-meta-path synergy.
Experimental results on real-world datasets validate the model's effectiveness.

Conclusions:

The probabilistic approach offers a powerful framework for HIN relevance.
The proposed relevance measure enhances network mining capabilities by considering cross-meta-path synergy.
The developed model provides a tailored and effective solution for HIN relevance measurement.