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Social networks are the social structures which are composed of people andtheir relationships and nowadays, play an important role in data extension. Insuch networks, the communities are recognized as the groups of users who areoften interacting with each other. In this article, a method will be introducedfor community detection, which has the capability of adoption with differentkinds of social networks and also is synchronized with the actual world. One ofthe most important defined parameters in this paper is the rate of thetransferred messages between the nodes of the network, this parameter would bedynamically investigated. In this strategy, the network is reviewed indifferent time intervals, and the inter-node relations are enhanced orweakened. Therefore, the topology of the network is continuously changing inresponse to the behavior of the users. The defined parameters in the proposedalgorithm are capable of adopting with different types of the social networksand a weight will be assigned to every parameter which is indicative of therelative importance of that parameter in comparison with the other ones. Theobtained results show that this method, in comparison with the similar methods,leads to achievement of the desirable results.

A new method for community detection in social networks based on message distribution