An Overview of Heterogeneous Social Network Analysis

Abstract

Heterogeneous Social Networks (HSNs) represent complex structures where diverse entities, such as users, items, and interactions, coexist and interact within a unified framework. This paper offers a systematic review of HSN Analysis, addressing the theoretical and practical challenges associated with investigating the interplay between varied node types and diverse relationships within HSNs. The paper begins by defining HSNs and outlining their characteristics, highlighting the existence of diverse entity kinds and a range of relationship types. It explores the significance of HSNs in modeling real‐world systems, including online social platforms, biological networks, e‐commerce networks, and recommendation systems, where diverse entities play distinct roles. The analysis of HSNs extends beyond traditional homogeneous networks, incorporating various types of nodes and edges, and introduces novel considerations for effective analysis. The difficulties in modeling, representing, and analyzing HSNs will be covered in this work. Several reviews of social network analysis have been published in the past, but they often focus on simple networks, not HSN analysis specifically. This paper aims to fill that gap by comprehensively reviewing different aspects of HSN and its analysis. We start with the fundamentals of HSNs, explore its major types‐multi‐relational networks and multi‐modal networks and further their impact on popular data mining tasks. Then, we explore various applications of heterogeneous information network analysis, like recommender systems, text mining, fraud detection, and e‐commerce. Finally, we look at recent research and suggest promising future directions in the field of HSN analysis.