Community Detection in Bipartite Networks: Algorithms and Case studies

2015 ◽  
pp. 25-50 ◽  
Author(s):  
Taher Alzahrani ◽  
K. J. Horadam
Keyword(s):  
Case Studies ◽  
Community Detection ◽  
Bipartite Networks

A uniform framework of projection and community detection for one-mode network in bipartite networks

2017 ◽  
Vol 26 (12) ◽  
pp. 128901 ◽  
Author(s):  
Guolin Wu ◽  
Changgui Gu ◽  
Lu Qiu ◽  
Huijie Yang
Keyword(s):  
Community Detection ◽  
Bipartite Networks

scholarly journals Mathematical Model and Algorithm for Link Community Detection in Bipartite Networks

2015 ◽  
Vol 05 (05) ◽  
pp. 421-434 ◽  
Author(s):  
Zhenping Li ◽  
Shihua Zhang ◽  
Xiangsun Zhang
Keyword(s):  
Mathematical Model ◽  
Community Detection ◽  
Bipartite Networks

scholarly journals A Bayesian Inference Method Using Monte Carlo Sampling for Estimating the Number of Communities in Bipartite Networks

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Guo-Zheng Wang ◽  
Li Xiong ◽  
Hu-Chen Liu
Keyword(s):  
Monte Carlo ◽  
Bayesian Inference ◽  
Probability Distribution ◽  
Community Detection ◽  
Degree Distribution ◽  
Prior Probability ◽  
Bipartite Networks ◽  
Inference Method ◽  
Prior Probability Distribution ◽  
Bayesian Inference Method

Community detection is an important analysis task for complex networks, including bipartite networks, which consist of nodes of two types and edges connecting only nodes of different types. Many community detection methods take the number of communities in the networks as a fixed known quantity; however, it is impossible to give such information in advance in real-world networks. In our paper, we propose a projection-free Bayesian inference method to determine the number of pure-type communities in bipartite networks. This paper makes the following contributions: (1) we present the first principle derivation of a practical method, using the degree-corrected bipartite stochastic block model that is able to deal with networks with broad degree distributions, for estimating the number of pure-type communities of bipartite networks; (2) a prior probability distribution is proposed over the partition of a bipartite network; (3) we design a Monte Carlo algorithm incorporated with our proposed method and prior probability distribution. We give a demonstration of our algorithm on synthetic bipartite networks including an easy case with a homogeneous degree distribution and a difficult case with a heterogeneous degree distribution. The results show that the algorithm gives the correct number of communities of synthetic networks in most cases and outperforms the projection method especially in the networks with heterogeneous degree distributions.

scholarly journals Community Detection in Airline Networks: An Empirical Analysis of American vs. Southwest Airlines

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Weiwei Wu ◽  
Haoyu Zhang ◽  
Shengrun Zhang ◽  
Frank Witlox
Keyword(s):  
Case Studies ◽  
Empirical Analysis ◽  
Community Detection ◽  
Topological Structure ◽  
Community Structures ◽  
Operation Pattern ◽  
Airline Networks ◽  
Modularity Maximization ◽  
Influential Nodes ◽  
Southwest Airlines

In this paper, we develop a route-traffic-based method for detecting community structures in airline networks. Our model is both an application and an extension of the Clauset-Newman-Moore (CNM) modularity maximization algorithm, in that we apply the CNM algorithm to large airline networks, and take both route distance and passenger volumes into account. Therefore, the relationships between airports are defined not only based on the topological structure of the network but also by a traffic-driven indicator. To illustrate our model, two case studies are presented: American Airlines and Southwest Airlines. Results show that the model is effective in exploring the characteristics of the network connections, including the detection of the most influential nodes and communities on the formation of different network structures. This information is important from an airline operation pattern perspective to identify the vulnerability of networks.

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