A New Method for Community Detection in the Complex Network on the Basis of Similarity

Introduction: Regarding complex network, to find optimal communities in the network has become a key topic in the field of network theory. It is crucial to understand the structure and functionality of associated networks. In this paper, we propose a new method of community detection that works on the Structural Similarity of a Network (SSN). Methods: This method works in two steps, in the first step, it removes edges between the different groups of nodes which are not very similar to each other. As a result of edge removal, the network is divided into many small random communities, which are referred to as main communities. Results: In the second step, we apply the Evaluation Method (EM), it chooses the best quality com-munities, from all main communities which are already produced in the first step. Lastly, we apply evaluation metrics to our proposed method and benchmarking methods, which show that the SSN method can detect comparatively more accurate results than other methods in this paper. Discussion: This approach is defined on the basis of the unweighted network, so in further research, it could be used on weighted networks and can explore some new deep-down attributes. Further-more, it will be used for Facebook and twitter weighted data with the artificial intelligence approach. Conclusion: In this article, we proposed a novel method for community detection in networks, called Structural Similarity of Network (SSN). It works in two steps. In the first step, it randomly removes low similarity edges from the network, which makes several small disconnected communities, called as main communities. Afterward, the main communities are merged to search for the final communi-ties, which are near to actual existing communities of the network. © 2022 Bentham Science Publishers.