  Social networks are the social structures which are composed of people and
their relationships and nowadays, play an important role in data extension. In
such networks, the communities are recognized as the groups of users who are
often interacting with each other. In this article, a method will be introduced
for community detection, which has the capability of adoption with different
kinds of social networks and also is synchronized with the actual world. One of
the most important defined parameters in this paper is the rate of the
transferred messages between the nodes of the network, this parameter would be
dynamically investigated. In this strategy, the network is reviewed in
different time intervals, and the inter-node relations are enhanced or
weakened. Therefore, the topology of the network is continuously changing in
response to the behavior of the users. The defined parameters in the proposed
algorithm are capable of adopting with different types of the social networks
and a weight will be assigned to every parameter which is indicative of the
relative importance of that parameter in comparison with the other ones. The
obtained results show that this method, in comparison with the similar methods,
leads to achievement of the desirable results.
