HRank: A Path based Ranking Framework in Heterogeneous Information Network

TL;DR

This paper introduces HRank, a framework for ranking objects in heterogeneous information networks by considering multiple object types and meta paths, using tensor analysis and constrained meta paths to capture rich semantics.

Contribution

The paper presents a novel path-based ranking framework that evaluates importance of objects and meta paths simultaneously in heterogeneous networks.

Findings

HRank effectively evaluates object and path importance.

Constrained meta paths improve ranking accuracy.

Experiments on real datasets validate the framework's effectiveness.

Abstract

Recently, there is a surge of interests on heterogeneous information network analysis. As a newly emerging network model, heterogeneous information networks have many unique features (e.g., complex structure and rich semantics) and a number of interesting data mining tasks have been exploited in this kind of networks, such as similarity measure, clustering, and classification. Although evaluating the importance of objects has been well studied in homogeneous networks, it is not yet exploited in heterogeneous networks. In this paper, we study the ranking problem in heterogeneous networks and propose the HRank framework to evaluate the importance of multiple types of objects and meta paths. Since the importance of objects depends upon the meta paths in heterogeneous networks, HRank develops a path based random walk process. Moreover, a constrained meta path is proposed to subtly capture the rich semantics in heterogeneous networks. Furthermore, HRank can simultaneously determine the importance of objects and meta paths through applying the tensor analysis. Extensive experiments on three real datasets show that HRank can effectively evaluate the importance of objects and paths together. Moreover, the constrained meta path shows its potential on mining subtle semantics by obtaining more accurate ranking results.