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).
Method:
This method works in two steps, at 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 as main communities.
Result:
In the second step, we apply the evaluation method (EM), it chooses the best quality communities, from all main
communities which already produced at the first step. At last, 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.
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 communities, which are near to actual existing communities of the network.
Discussion:
This approach is defined on the base of the unweighted network, so in Further research it could be used on
weighted networks and can explore some new deep-down attributes. Furthermore, it will be used Facebook and twitter
weighted data with the artificial intelligence approach.
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