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CANDY: An Efficient Framework for Updating Properties on Large Scale Dynamic Networks
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DescriptionQueries on large graphs use the stored graph properties to generate responses. As most of the real-world graphs are dynamic, i.e., the graph topology changes with time, and hence the related graph properties are also time-varying. In such cases, maintaining correctness in stored graph properties requires recomputation or update on previous properties. Here, we present an efficient framework, CANDY for updating the properties in large dynamic networks. We prove the efficacy of our general framework by applying it to update graph properties such as Single Source Shortest Path (SSSP), Vertex Coloring, and PageRank. Empirically we show that our shared-memory parallel and NVIDIA GPU-based data-parallel implementations perform better than the state-of-the-art implementations.