Community detection in social networks 论文

详细信息

发表期刊/会议

Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery

发表日期

2016-02-19

发表年份

2016

摘要

The expansion of the web and emergence of a large number of social networking sites ( SNS ) have empowered users to easily interconnect on a shared platform. A social network can be represented by a graph consisting of a set of nodes and edges connecting these nodes. The nodes represent the individuals/entities, and the edges correspond to the interactions among them. The tendency of people with similar tastes, choices, and preferences to get associated in a social network leads to the formation of virtual clusters or communities. Detection of these communities can be beneficial for numerous applications such as finding a common research area in collaboration networks, finding a set of likeminded users for marketing and recommendations, and finding protein interaction networks in biological networks. A large number of community‐detection algorithms have been proposed and applied to several domains in the literature. This paper presents a survey of the existing algorithms and approaches for the detection of communities in social networks. We also discuss some of the applications of community detection. WIREs Data Mining Knowl Discov 2016, 6:115–135. doi: 10.1002/widm.1178 This article is categorized under: Algorithmic Development > Structure Discovery