A comparative study of some algorithms for detecting communities in social networks

In this paper, we studied some methods used in community detection in social networks. In the context of social networks, a community is a set of entities with a lot of interactions among them and little interaction with other sets outside. There are approaches related to static social networks, and others which focus on the dynamic social networks whose structure (actors and links) evolves over time. Static community detection approaches are able to find a division only if a graph is defined for a given time. However, many real graphs have the property to change and evolve over time. That is some nodes and links can appear or disappear during this process of evolution. In order to find communities in such networks we must take into account their different stages of evolution to provide coherent communities not just in a particular point in time, but all along their possible modifications over time. In this paper, we studied the static and dynamic approaches and we made a comparison between different algorithms using several real datasets.