GraphIdx: An efficient indexing technique for accelerating graph data mining

Many graph mining algorithms process large graphs with several passes and suffers from huge I/O cost. GraphIdx, an open-source C library, facilitates a memory-efficient indexing of large graphs to reduce that I/O cost. GraphIdx indexes a block of graph data for a set of nodes based on the empirical evaluation of edges. Due to the indexed graph, graph mining algorithms can access and process only the related nodes and their edges instead of scanning entire graph. As a result, the number of I/Os is significantly reduced. Moreover, GraphIdx accredited algorithms can process graphs in parallel due to the indexed data. •An algorithm to construct an index table for accessing graph data selectively.•Avoid full scan to read a part of the graph and reduce the I/O cost significantly.•Parallelize graph mining algorithms by processing indexed data simultaneously.•An open-source C library to construct and exploit indices for efficient graph mining. [Display omitted]