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• W2950343052 abstract "We consider the enumeration of maximal bipartite cliques (bicliques) from a large graph, a task central to many practical data mining problems in social network analysis and bioinformatics. We present novel parallel algorithms for the MapReduce platform, and an experimental evaluation using Hadoop MapReduce. Our algorithm is based on clustering the input graph into smaller sized subgraphs, followed by processing different subgraphs in parallel. Our algorithm uses two ideas that enable it to scale to large graphs: (1) the redundancy in work between different subgraph explorations is minimized through a careful pruning of the search space, and (2) the load on different reducers is balanced through the use of an appropriate total order among the vertices. Our evaluation shows that the algorithm scales to large graphs with millions of edges and tens of mil- lions of maximal bicliques. To our knowledge, this is the first work on maximal biclique enumeration for graphs of this scale." @default.
• W2950343052 created "2019-06-27" @default.
• W2950343052 creator A5042545880 @default.
• W2950343052 creator A5047034711 @default.
• W2950343052 date "2014-04-19" @default.
• W2950343052 modified "2023-09-27" @default.
• W2950343052 title "Enumerating Maximal Bicliques from a Large Graph using MapReduce" @default.
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• W2950343052 hasPublicationYear "2014" @default.
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