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- W2162866953 abstract "Graph has been a ubiquitous and essential data representation to model real world objects and their relationships. Today, large amounts of graph data have been generated by various applications. Graph summarization techniques are crucial in uncovering useful insights about the patterns hidden in the underlying data. However, all existing works in graph summarization are single-process solutions, and as a result cannot scale to large graphs. In this paper, we introduce three distributed graph summarization algorithms to address this problem. Experimental results show that the proposed algorithms can produce good quality summaries and scale well with increasing data sizes. To the best of our knowledge, this is the first work to study distributed graph summarization methods." @default.
- W2162866953 created "2016-06-24" @default.
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- W2162866953 date "2014-11-03" @default.
- W2162866953 modified "2023-09-27" @default.
- W2162866953 title "Distributed Graph Summarization" @default.
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- W2162866953 doi "https://doi.org/10.1145/2661829.2661862" @default.
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