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Most of community detection algorithms are designed from the perspective of nodes, which usually neglect the overlapping structure in networks. Whereas some of them hold the weakness of high complexity, inaccuracy and low stability. To solve the above issues, an overlapping community and node discovery algorithm based on edge similarity is proposed. In this paper, is established according to the incidence matrix. Then the algorithm is proceeding on line graph and finally the community detection results are restored to the original network, thus overlapping community and nodes are discovered. Several experiments are carried out on different datasets, demonstrating that the proposed algorithm is effective. Introduction Most of the existing community detection algorithms [1] in complex network [2] are designed from the perspective of nodes, in which a certain node can be divided into only one community. However, in real networks, some nodes usually belong to several communities, which are known as overlapping nodes [3] (or overlapping community [4]).For the overlapping nodes are related to many communities simultaneously, they usually act as a bridge between different communities and play an important role in networks. In the face of increasing scale of networks, further research on overlapping community and node discovery is of great significance. Mining overlapping nodes in networks is usually achieved by discovering overlapping communities [5]. Although these researches had some achievements in the past, it is still lacking. Pallaet al. [6] proposed the famous Clique Percolation Method in 2005, however, the main problem is that a parameter k is difficult to determine. Gregory put forward the CONGA [7] algorithm, where as the effect of the algorithm heavily depends on the empirical parameters. Generally, as for the current algorithm of overlapping nodes detection, there are many problems such as high complexity, low accuracy etc. To solve the above issues, this paper proposes an overlapping community and node discovery algorithm based on edge similarity. The object of study is transformed from node to edge, and then the overlapping nodes are discovered by the conversion between the line graph and the original network. Algorithm Description Line Graph Usually, the network is represented by the adjacency matrix of the node, but the adjacency matrix can only describe the relation between the nodes in the network graph. In order to express the relation between two edges, it is necessary to be mutually transformed with the incidence matrix.

Overlapping Community and Node Discovery Algorithm Based on Edge Similarity