Contents lists available at ScienceDirect Information Processing and Management journal homepage: www.elsevier.com/locate/infoproman Tracking community evolution in social networks: A survey Narimene Dakiche ⁎ ,a,1 , Fatima Benbouzid-Si Tayeb a,1 , Yahya Slimani b , Karima Benatchba a,1 a Laboratoire des Méthodes de Conception de Systèmes (LMCS), Ecole nationale Supérieure d’Informatique (ESI), BP 68M -16 270 Oued Smar, Alger, Algérie b Computer Science Department, ISAMM Institute of Manouba, 2010 Manouba. ARTICLE INFO Keywords: Social network Dynamic network Dynamic community detection Community evolution ABSTRACT This paper presents a survey of previous studies done on the problem of tracking community evolution over time in dynamic social networks. This problem is of crucial importance in the field of social network analysis. The goal of our paper is to classify existing methods dealing with the issue. We propose a classification of various methods for tracking community evolution in dy- namic social networks into four main approaches using as a criterion the functioning principle: the first one is based on independent successive static detection and matching; the second is based on dependent successive static detection; the third is based on simultaneous study of all stages of community evolution; finally, the fourth and last one concerns methods working di- rectly on temporal networks. Our paper starts by giving basic concepts about social networks, community structure and strategies for evaluating community detection methods. Then, it de- scribes the different approaches, and exposes the strengths as well as the weaknesses of each. 1. Introduction Networks underlie many complex phenomena involving pairwise interactions between entities (Easley & Kleinberg, 2010; Kolaczyk, 2009). Different examples can be given: internet, online social networks such as Facebook or Twitter, cellphone com- munications, hyperlinks in blogs, and electric power grids. Over the last years, Social Networks (SN) have received significant attention due to their increasing importance and popularity. A social network for an individual is created with his/her interactions and personal relationships with other members in the society. With the fast web expansion, there is a tremendous growth of online users’ interactions. People extend their social life in unprecedented ways as it is much easier to find online friends with similar interests. These social networks have interesting patterns and properties, which gave rise to the very active field of Social Network Analysis (SNA). The main aim of SNA is to understand the relationship between actors involved in an interaction by exploiting network and graph theories for numerous useful purposes. One important characteristic of Social Networks is the community structure, i.e., groups of individuals with high density inter- actions among individuals of the same group and comparatively low density interactions among individuals of different groups (Leskovec, Lang, Dasgupta, & Mahoney, 2008). The process of discovering these groups is known as community detection and is a task of fundamental importance in social network analysis (Tang & Liu, 2010). Community detection frequently discloses deeper properties of networks; it provides meaningful insights of the networks’ internal structure as well as its organization principles. This task can be useful in many applications where group decisions are taken, e.g., multicasting a message of interest or recommending a https://doi.org/10.1016/j.ipm.2018.03.005 Received 31 July 2017; Received in revised form 10 March 2018; Accepted 12 March 2018 ⁎ Corresponding author. 1 http://www.esi.dz. E-mail address: an_dakiche@esi.dz (N. Dakiche). Information Processing and Management xxx (xxxx) xxx–xxx 0306-4573/ © 2018 Elsevier Ltd. All rights reserved. Please cite this article as: Dakiche, N., Information Processing and Management (2018), https://doi.org/10.1016/j.ipm.2018.03.005