Efficient algorithms for parameter-free edge structural diversity search on graphs

Abstract

Edge structural diversity refers to the complexity of the social context in the common neighbors of a pair of vertices, which can be used as an important indicator of the spread of information and social influence. The existing edge structural diversity score is designed based on the given threshold parameter, which is easy to vary with the parameter, leading to unstable results. In this paper, we propose an edge diversity model without the threshold parameter to get stable results, based on which we propose two query problems, top-k and skyline edge search. We propose the upper-bound online algorithms, which obtain results by computing exact diversity scores for partial edges. Then, we propose the basic index. Based on this basic index we can obtain edges of each ego-network directly, without extracting the ego-network from the original graph. Further, we propose the optimized index, which maps edges in the original graph as super-vertices and records information using super-edges, reducing the index size. Finally, we conduct experiments on 12 real-world datasets. The experimental results verify the effectiveness and efficiency of our algorithms.