| Summary: | Abstract Massive graphs are ubiquitous and at the heart of many real-world problems and applications ranging from the World Wide Web to social networks. As a result, techniques for compressing graphs have become increasingly important and remains a challenging and unsolved problem. In this work, we propose a graph compression and encoding framework called GraphZIP based on the observation that real-world graphs often form many cliques of a large size. Using this as a foundation, the proposed technique decomposes a graph into a set of large cliques, which is then used to compress and represent the graph succinctly. In particular, disk-resident and in-memory graph encodings are proposed and shown to be effective with three important benefits. First, it reduces the space needed to store the graph on disk (or other permanent storage device) and in-memory. Second, GraphZIP reduces IO traffic involved in using the graph. Third, it reduces the amount of work involved in running an algorithm on the graph. The experiments demonstrate the scalability, flexibility, and effectiveness of the clique-based compression techniques using a collection of networks from various domains.
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