HeteSim: A General Framework for Relevance Measure in Heterogeneous Networks

TL;DR

HeteSim is a versatile relevance measure designed for heterogeneous networks, capable of evaluating relatedness between objects of same or different types using path constraints, with proven efficiency and effectiveness.

Contribution

The paper introduces HeteSim, a novel, semi-metric relevance measure for heterogeneous networks that unifies relatedness assessment across object types and provides efficient computation strategies.

Findings

HeteSim effectively measures relatedness in heterogeneous networks.

HeteSim is computationally efficient for large-scale data.

Empirical results validate HeteSim's accuracy and utility.

Abstract

Similarity search is an important function in many applications, which usually focuses on measuring the similarity between objects with the same type. However, in many scenarios, we need to measure the relatedness between objects with different types. With the surge of study on heterogeneous networks, the relevance measure on objects with different types becomes increasingly important. In this paper, we study the relevance search problem in heterogeneous networks, where the task is to measure the relatedness of heterogeneous objects (including objects with the same type or different types). A novel measure HeteSim is proposed, which has the following attributes: (1) a uniform measure: it can measure the relatedness of objects with the same or different types in a uniform framework; (2) a path-constrained measure: the relatedness of object pairs are defined based on the search path that connect two objects through following a sequence of node types; (3) a semi-metric measure: HeteSim has some good properties (e.g., self-maximum and symmetric), that are crucial to many data mining tasks. Moreover, we analyze the computation characteristics of HeteSim and propose the corresponding quick computation strategies. Empirical studies show that HeteSim can effectively and efficiently evaluate the relatedness of heterogeneous objects.