A survey about community detection over On-line Social and Heterogeneous Information Networks

In modern Online Social Networks (OSNs), the need to detect users' communities based on their interests and social connections has became a more and more important challenge in literature. Community Detection supports and make more effective and efficient several Social Network Analysis (SNA) applications: the diffusion of a new idea or technologies can be maximized by identifying of people group interested about a given topic, the recommendation suggestion can be improved taking in account also how the social ties can be influenced the user chooses and the behaviors of people in the same communities, expert finding tasks could be more accurate if users are earlier subdivided into thematic groups, and so on. This paper presents a survey that provides a comprehensive and comparative study of all the different community detection techniques applicable to the various models proposed for OSNs. In particular, the most diffused approaches based on game theory, artificial intelligence and fuzzy strategies are detailed and compared, highlighting the related pros and cons. In addition, the problem of their applicability on the different OSN models is discussed, focusing on complex networks. Finally, the main open issues and challenges for the community detection problem are reported to address the futures work concerning this topic. (C) 2021 Elsevier B.V. All rights reserved.