Memory-based label propagation algorithm for community detection in social networks

Abstract

Community detection in social network is a significant issue in the study of the structure of a network and understanding its characteristics. A community is a significant structure formed by nodes with more connections between them. In recent years, several algorithms have been presented for community detection in social networks among them label propagation algorithm is one of the fastest algorithms, but due to the randomness of the algorithm its performance is not suitable. In this paper, we propose an improved label propagation algorithm called memory-based label propagation algorithm (MLPA) for finding community structure in social networks. In the proposed algorithm, a simple memory element is designed for each node of graph and this element store the most frequent common adoption of labels iteratively. Our experiments on the standard social network datasets show a relative improvement in comparison with other community detection algorithms.