Building Smaller Sized Surrogate Models of Complex Bipartite Networks Based on Degree Distributions

Abstract

This paper presents an approach for generating surrogate bipartite networks with varying sizes based on degree distributions of given bipartite networks. The resulting surrogate networks can be used for problems such as design of algorithms for similarity search, community detection and clustering, and recommender systems. The primary advantage of using smaller surrogate networks over original large-scale networks is the reduction in associated computational expense. Degree distribution is chosen because of its widespread acceptance, simplicity, and prior literature suggesting its ability to better capture large-scale network properties. The approach is illustrated using a bipartite network from an open-source software development repository. The network consists of nodes representing people and projects, and edges representing people working on different projects. A comparison between the surrogate networks and the original networks is presented. The results show that the resized networks obtained using the proposed approach can be used to match the original degree distribution. A comparison of seven other network characteristics is also provided.