Cohesive Subgraph Search over Big Heterogeneous Information Networks: Applications, Challenges, and Solutions

Abstract

With the advent of a wide spectrum of recent applications, querying heterogeneous information networks (HINs) has received a great deal of attention from both academic and industrial societies. HINs involve objects (vertices) and links (edges) that are classified into multiple types; examples include bibliography networks, knowledge networks, and user-item networks in E-business. An important component of these HINs is the cohesive subgraph, or a subgraph containing vertices that are densely connected internally. Searching cohesive subgraphs over HINs has found many real applications, such as community search, product recommendation, fraud detection, and so on. Consequently, how to design effective cohesive subgraph models and how to efficiently search cohesive subgraphs on large HINs become important research topics in the era of big data. In this tutorial, we first highlight the importance of cohesive subgraph search over HINs in various applications and the unique challenges that need to be addressed. Subsequently, we conduct a thorough review of existing works of cohesive subgraph search over HINs. Then, we analyze and compare the models and solutions in these works. Finally, we point out new research directions. We believe that this tutorial not only helps researchers to have a better understanding of existing cohesive subgraph search models and solutions, but also provides them insights for future study.