Truss-based Structural Diversity Search in Large Graphs (Extended Abstract)

Structural diversity, the multiplicity of social contexts inside an individual’s contact neighborhood, is shown to play an important role in the social contagion process. Existing models have limited decomposability for analyzing large-scale networks, which may suffer from the inaccurate reflection of social context diversity. In this paper, we propose a truss-based structural diversity model to address the limitations. We study the problem of top-r structural diversity search to find r vertices with the largest truss-based structural diversity scores in a graph. We propose two novel index structures of TSD-index and GCT-index, and efficient index-based query processing algorithms to solve the problem. Extensive experiments demonstrate the effectiveness and efficiency of our proposed model and algorithms, against state-of-the-art methods.