Electrical and Computer Engineering, Computer Science
2014 IEEE International Congress on Big Data
Link to Published Version
Journal or Book Title
Proceedings of the 2014 IEEE International Congress on Big Data
BIG DATA CONGRESS '14
June 27-July 2, 2014
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.
Mukherjee, Arko Provo and Tirthapura, Srikanta, "Enumerating Maximal Bicliques from a Large Graph using MapReduce" (2014). Electrical and Computer Engineering Conference Papers, Posters and Presentations. 53.