SLPA-IF1: Label Propagation Based Overlapping Community Detection

Detection of overlapping communities over a network is imperative due to its applicability in multiple domains starting from geographical to online networks. This paper proposes an effective overlapping community detection method SLPA-IF1. Initially, nodes label initialization is done during pre-processing of data. Label updation and propagation is performed during the evolution phase which consists of selection of listener node, speaker rule and listener rule. Speaker rule is modified to consider the mean of occurring frequency of labels instead of random label selection. We have also proposed a new measure named label specificity for listener rule which is calculated as the mean of occurring frequency of labels minus probability of occurrence of that label. The proposed method leads to more accurate label selection during detection of communities over a network. The run time computation has shown the scalability of the proposed method with respect to increasing network size. For large scale networks, the computing time of the proposed method is less than other state-of-the-art methods.