I/O efficient ECC graph decomposition via graph reduction

VLDB Endowment
Publication Type:
Conference Proceeding
Proceedings of the 42nd International Conference on Very Large Data Bases, 2016, 9 (7), pp. 516 - 527
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© 2016 VLDB Endowment 21508097/16/03.The problem of computing k-edge connected components (k-ECCs) of a graph G for a specific k is a fundamental graph problemand has been investigated recently. In this paper, we study the problemof ECC decomposition, which computes the k-ECCs of a graphG for all k values. ECC decomposition can be widely applied ina variety of applications such as graph-topology analysis, communitydetection, Steiner component search, and graph visualization.A straightforward solution for ECC decomposition is to apply theexisting k-ECC computation algorithm to compute the k-ECCs forall k values. However, this solution is not applicable to large graphsfor two challenging reasons. First, all existing k-ECC computationalgorithms are highly memory intensive due to the complex datastructures used in the algorithms. Second, the number of possiblek values can be very large, resulting in a high computational costwhen each k value is independently considered. In this paper, weaddress the above challenges, and study I/O efficient ECC decompositionvia graph reduction. We introduce two elegant graph reductionoperators which aim to reduce the size of the graph loadedin memory while preserving the connectivity information of a certainset of edges to be computed for a specific k. We also proposethree novel I/O efficient algorithms, Bottom-Up, Top-Down, andHybrid, that explore the k values in different orders to reduce the redundantcomputations between different k values. We analyze theI/O and memory costs for all proposed algorithms. In our experiments, we evaluate our algorithms using seven real large datasetswith various graph properties, one of which contains 1:95 billionedges. The experimental results show that our proposed algorithmsare scalable and efficient.
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