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- W4288349888 abstract "Graph analytics for large scale graphs has gained interest in recent years. Many graph algorithms have been designed for vertex-centric distributed graph processing frameworks to operate on large graphs with 100 M vertices and edges, using commodity clusters and Clouds. Subgraph-centric programming models have shown additional performance benefits than vertex-centric models. But direct mapping of vertex-centric and shared-memory algorithms to subgraph-centric frameworks are either not possible, or lead to inefficient algorithms. In this paper, we present three subgraph-centric distributed graph algorithms for triangle counting, clustering and minimum spanning forest, using variations of shared and vertex-centric models. These augment existing subgraph-centric algorithms that exist in literature, and allow a broader evaluation of these three classes of graph processing algorithms and platforms." @default.
- W4288349888 created "2022-07-29" @default.
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- W4288349888 date "2019-05-20" @default.
- W4288349888 modified "2023-09-25" @default.
- W4288349888 title "Distributed Algorithms for Subgraph-Centric Graph Platforms" @default.
- W4288349888 doi "https://doi.org/10.48550/arxiv.1905.08051" @default.
- W4288349888 hasPublicationYear "2019" @default.
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