Neighbor Similarity Based Agglomerative Method for Community Detection in Networks

Community structures can reveal organizations and functional properties of complex networks; hence, detecting communities from networks is of great importance. With the surge of large networks in recent years, the efficiency of community detection is demanded critically. Therefore, many local methods have emerged. In this paper, we propose a node similarity based community detection method, which is also a local one consisted of two phases. In the first phase, we first take out the node with the largest degree from the network to take it as an exemplar of the first community and insert its most similar neighbor node into the community as well. Then, the one with the largest degree in the remainder nodes is selected; if its most similar neighbor has not been classified into any community yet, we create a new community for the selected node and its most similar neighbor. Otherwise, if its most similar neighbor has been classified into a certain community, we insert the selected node into the community to which its most similar neighbor belongs. This procedure is repeated until every node in the network is assigned to a community; at that time, we obtain a series of preliminary communities. However, some of them might be too small or too sparse; edges connecting to outside of them might go beyond the ones inside them. Keeping them as the final ones will lead to a low-quality community structure. Therefore, we merge some of them in an efficient approach in the second phase to improve the quality of the resulting community structure. To testify the performance of our proposed method, extensive experiments are performed on both some artificial networks and some real-world networks. The results show that the proposed method can detect high-quality community structures from networks steadily and efficiently and outperform the comparison algorithms significantly.