The social networks of today are a set of massive, dynamically changing graph structures. Each of these graphs contain a set of nodes (individuals) and a set of edges among the nodes (relationships). The choice of representation of a graph determines what information is easy to obtain from it. However, many social network graphs are so large that even their basic representations (e.g. adjacency lists) do not fit in main memory. Hence an ongoing field of study has focused on designing compressed representations of graphs that facilitate certain query functions.This work is based on representing dynamic social networks that we call streaming graphs where edges stream into our compressed representation. The crux of this work is the use of a novel data structure for streaming graphs that is based on an indexed array of compressed binary trees that builds the graph directly without using any temporary storage structures. We provide fast access methods for edge existence (does an edge exist between two nodes?), neighbor queries (list a node’s neighbors), and streaming operations (add/remove nodes/edges). We test our algorithms on public, anonymized, massive graphs such as Friendster, LiveJournal, Pokec, Twitter, and others. Our empirical evaluation is based on several parameters including time to compress, memory required by the compression algorithm, size of compressed graph, and time to execute queries. Our experimental results show that our current approach outperforms previous approaches in various key respects such as compression time, compression memory, compression ratio, and query execution times and hence the best to date overall.
Sekharan, Chandra N.; Radhakrishnan, Sridhar; Nelson, Ben; and Chatterjee, Amlan. Queryable Compression for Massively Streaming Social Networks. , , : , 2017. Retrieved from Loyola eCommons, Computer Science: Faculty Publications and Other Works,
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