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 millions of maximal bicliques. To our knowledge, this is the first work on maximal biclique enumeration for graphs of this scale.
Available at: http://works.bepress.com/srikanta-tirthapura/22/
This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Mukherjee, Arko Provo, and Srikanta Tirthapura. "Enumerating Maximal Bicliques from a Large Graph Using MapReduce." In Proceedings of the 2014 IEEE International Congress on Big Data, pp. 707-716. IEEE Computer Society, 2014. DOI: 10.1109/BigData.Congress.2014.105. Posted with permission.