On efficiently summarizing a large dynamic graph

Graph summarization is a well known technique to create summary of mega-sized structures like social networks and world wide web. A prime bottleneck in this process is in-efficient pairwise similarity computation strategy to find similar nodes for compression. Previous work provides a scalable similarity computation strategy by using Locality Sensitive Hashing (LSH) to improve execution time of pairwise methods for summary of a static graph. Whereas LSH adoption provides desired acceleration, however, it requires large storage space for indexing candidate similar nodes. This problem becomes even more challenging in case of a dynamic graph, increasing the space complexity from O (b.n) to O (b.n.k), where b, n, and k are number of hash tables, total nodes, and snapshots of the graph respectively. In this paper, we propose a new index structure for LSH to align candidate similar nodes from a dynamic graph, with least storage space complexity. The proposed structure reduces space requirements by a factor η, where range of η depends on structural redundancy of graphs. We evaluate our proposed solution for summarization of four real world dynamic graphs and obtain compression upto 52%.