The topological characteristics and community structure in consumer-service bipartite graph

Abstract

We apply network analysis to study bipartite consumerservice graph that represents service transaction to understand consumer demand. Based on real-world computer log files of a library, we found that consumer graph projected from bipartite graph deviates significantly from theoretical predictions based on random bipartite graph. We observed smaller-than-expected average degree, larger-than-expected average path length and stronger-than-expected tendency to cluster. These findings motivated to explore the community structure of the network. As a result, the weighted consumer network showed significant community structure than the unweighted network. Communities picked out by the algorithm revealed that individuals in the same community were due to their common specialties or the overlapping structure of knowledge between their specialties. © 2009 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.