Authors: Aashish Pandey (University of North Texas), Arindam Khanda (Missouri University of Science and Technology), Sriram Srinivasan (University of Oregon), Sanjukta Bhowmick (University of North Texas), Sajal Das (Missouri University of Science and Technology), and Boyana Norris (University of Oregon)
Abstract: Queries 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.
Best Poster Finalist (BP): no
Poster summary: PDF
Back to Poster Archive Listing