Enhanced Algorithms for Counting Rectangles in Large Bipartite Graphs using MapReduce

Rectangles for bipartite graphs are like triangles for unipartite graphs as both represent the smallest cycles in such graphs. Rectangle Counting is considered an important task in many bipartite network analysis metrics and is considered the core of computing such metrics, especially in cluster coefficient, bitruss, etc. However, there are few efficient algorithms to deal with this problem, especially in a large bipartite graph. In this work, we use MapReduce to enhance an algorithm to count rectangles in a large bipartite graph. The results show that our proposed MapReduce-based algorithm gives a better execution time than the existing algorithms, especially when it is applied in very large bipartite graphs.