Scanning Approach and its Extension for Community Detection in Social Networks

Abstract

Network data have become very popular with the growth of technologies and social applications such as Twitter and Facebook. Community plays one of the most important roles in social networks. Although many algorithms for community detection have been developed based on the features of community structures, most of them suffer computation problems when dealing with a large-scale network. In this talk, I will introduce the scanning approach and its extension for community detection, which are developed to systematically search communities in a large-scale network. Compared to the iterative approaches, our methods are easily embedded in large-scale networks without suffering many computing problems. In addition, I will illustrate our approaches with some famous social networks. Compared to some existing algorithms, our approaches are comparable in detection accuracy and more flexible to be adapted to different criteria and large-scale networks. Keywords: Community Detection, Scanning Approach, Pairwise Scanning Algorithm, Likelihood-based Criterion, Hierarchical Structure, Stepwise Algorithm