Deployment of Information Diffusion for Community Detection in Online Social Networks: A Comprehensive Review

作     者：Das, Soumita Biswas, Anupam 

作者机构：Natl Inst Technol Silchar Dept Comp Sci & Engn Silchar 788010 India 

出 版 物：《IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS》 (IEEE Trans. Computat. Soc. Syst.)

年 卷 期：2021年第8卷第5期

页      面：1083-1107页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Diffusion processes Social networking (online) Image edge detection Detection algorithms Topology Mathematical model Dispersion Community detection disjoint communities information diffusion online social networks (OSNs) overlapping communities 

摘      要：The flow of information through active users in online social networks (OSNs) plays a major role in forming natural social groups, popularly known as communities. Although structural and topological aspects of the network had been central to most of the community detection approaches, incorporation of information flow for community detection has been an emerging topic in the recent past. Often, the flow of information is studied as a traceable process called information diffusion. The flow of information in the network affects various factors like temporal characteristics, network attributes, or social attributes. The information diffusion process helps to extract this information including where and when information is generated and in what fashion the dispersion occurs. Thus, it has the potential to aid the community detection process in social networks. In this article, the deployment of the information diffusion process for community detection has been studied extensively. The study is mainly focused on how information flow affects various network properties and social facets and explored the possibility of deployment for community detection. Various information diffusion models and community detection algorithms have been discussed in the context of network properties and social facets. Current challenges, future directions, and modalities for the deployment of information diffusion in community detection have been discussed. In addition, various widely used datasets, evaluation metrics as well as evaluation methods for evaluating community detection algorithms are also detailed.