Abstract
In recent years, several approaches have been proposed in order to detect communities in social networks. Most of them suffer from the recurrent problems: no detection of overlapping communities, exponential running time, no detection of all possible communities transformations, don’t consider the properties of social members, inability to deal with large scale networks, etc. Multi-agent systems are very suitable for modeling the phenomena in which various autonomous entities in inter-actions able to evolve in a dynamic environment. Considering the advantages of multi-agent simulations for social networks, in the present study, an incremental multi-agent system based on electric field is proposed. In this approach, a group of autonomous agents work together to discover the dynamic communities. Indeed, an agent is associated to each detected community. To update its community according to the dynamic of its members, each agent creates an electric field around it. It applies an attractive force to add very connected and similar members and neighboring communities. In the same time, it applies a repulsive force to reject some members and to get away from other communities. These forces are based on the structural and attributes similarity. To study the performance of this approach, set of different experiments is performed. The obtained results show the efficiency of the proposed model that was able to overcome all mentioned problems.
Highlights
Since their introduction, social networks sites such as Facebook, Instagram and Google+ have attracted millions of users, many of whom have integrated these sites into their daily practices
Since social networks are usually modeled by a graph such as nodes represent social actors and edges represent the relationships, a community is defined as a set of nodes that are densely connected among themselves and sparsely connected to the rest of nodes
From these simulations on artificial networks, we can conclude that M ASEF performed well for distinct types of graphs, and it was always able to compute the exact structure of each network regardless of its nature and complexity
Summary
Social networks sites such as Facebook, Instagram and Google+ have attracted millions of users, many of whom have integrated these sites into their daily practices. Since social networks are usually modeled by a graph such as nodes represent social actors and edges represent the relationships, a community is defined as a set of nodes that are densely connected among themselves and sparsely connected to the rest of nodes. These communities evolve over time according to the evolution of the actors and their interactions.
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