A graph compression algorithm for large-scale web-like graphs (web/social networks/citation graphs)
An efficient graph compression algorithm for large-scale graphs that exploits the graph’s structure to achieve better compression rate. In particular, it makes use of the locality of reference in the graph and the power law distribution of its nodes’ degrees, two properties usually observed in large sparse graphs that model networks created by human activity (eg. the web, social networks or citation graphs). Furthermore, this approach focuses on navigating through both the incoming and outgoing edges of each node of the compressed graph in linear time.
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