Title :
Rectangle Counting in Large Bipartite Graphs
Author :
Jia Wang ; Fu, Ada Wai-Chee ; Cheng, James
Author_Institution :
Dept. of Comput. Sci. & Eng., Chinese Univ. of Hong Kong, Hong Kong, China
fDate :
June 27 2014-July 2 2014
Abstract :
Rectangles are the smallest cycles (i.e., cycles of length 4) and most elementary sub-structures in a bipartite graph. Similar to triangle counting in uni-partite graphs, rectangle counting has many important applications where data is modeled as bipartite graphs. However, efficient algorithms for rectangle counting are lacking. We propose three different types of algorithms to cope with different data volumes and the availability of computing resources. We verified the efficiency of our algorithms with experiments on both large real-world and synthetic bipartite graphs.
Keywords :
computational complexity; graph theory; bipartite graph substructures; computing resource availability; cycle length; data modelling; data volumes; large-real-world bipartite graphs; large-synthetic bipartite graphs; rectangle counting; time complexity; Algorithm design and analysis; Bipartite graph; Clustering algorithms; Complexity theory; Equations; Parallel algorithms; Partitioning algorithms; bipartite graphs; rectangle counting;
Conference_Titel :
Big Data (BigData Congress), 2014 IEEE International Congress on
Conference_Location :
Anchorage, AK
Print_ISBN :
978-1-4799-5056-0
DOI :
10.1109/BigData.Congress.2014.13