Using Multi-Agent Collective Intelligence for Detecting Overlapping Communities in Social Networks

A social network is a social structure composed of a set of social actors (such as individuals or organizations) and a set of bilateral relationships between these actors. The social networking vision provides a clear way of analyzing the structure of the entire social network. The recognition of communities in networks is one of the major challenges in network science. One of the biggest concerns after community detection is to identify the main community of active nodes in the network that belong to several communities, this problem is still one of the interests of researchers in this field. Finding societies that overlap in social networks is an important topic in social networks analysis. The algorithm presented in this paper is based on the multi-agent particle swarm optimization as a collective intelligence due to the connection between several simple components which enables them to regulate their behavior and relationships with the rest of the group according to certain rules. As a result, self-organizing in collective activities can be seen, collective intelligence increases the speed and accuracy of global search, and uses a special type of coding to identify the number of communities. In this method, the modularity function is used as a fitness function to optimize particle swarm. Several experiments show that the proposed algorithm (MAPSOCD) is capable of detecting nodes in overlapping communities with high accuracy. The point in using the previously presented particle swarm optimization algorithms for community detection is that they are only able to recognize non-overlapping communities.

Keywords:

Particle swarm optimization , Clustering , Multi-agent , Graph clustering , Overlapping communities , Social networks