Set-based approach for lossless graph summarization using Locality Sensitive Hashing

Graph summarization is a valuable approach for in-memory processing of a big graph. A summary graph is compact, yet it maintains the overall characteristics of the underlying graph, thus suitable for querying and visualization. To summarize a big graph, the idea is to compress the similar nodes in dense regions of the graph. The existing approaches find these similar nodes either by nodes ordering or pair-wise similarity computations. The former approaches are scalable but cannot simultaneously consider the attributes and neighborhood similarity among the nodes. In contrast, the pair-wise summarization methods can consider both the similarity aspects but are impractical for a big graph. In this paper, we propose a set-based summarization method that aggregates the sets of similar nodes in each iteration, thus provides scalability. To find each set, we approximate the candidate similar nodes without nodes ordering and explicit similarity computations by using Locality Sensitive Hashing, LSH. In conjunction with an information theoretic approach, we present the scalable solutions for lossless summarization of both attributed and non-attributed graphs.