Bipartite graphs as models of complex networks

Authors:
Jean-Loup Guillaume;Matthieu Latapy
Affiliations:
LIAFA – CNRS, Université Paris 7, Paris, France;LIAFA – CNRS, Université Paris 7, Paris, France
Venue:
CAAN'04 Proceedings of the First international conference on Combinatorial and Algorithmic Aspects of Networking
Year:
2004

Citing 3
Cited 6

On maximum clique problems in very large graphs

External memory algorithms
The diameter of random massive graphs

SODA '01 Proceedings of the twelfth annual ACM-SIAM symposium on Discrete algorithms
Computers and Intractability: A Guide to the Theory of NP-Completeness

Computers and Intractability: A Guide to the Theory of NP-Completeness

Preface

Theoretical Computer Science - Complex networks
Relevance of massively distributed explorations of the internet topology: qualitative results

Computer Networks: The International Journal of Computer and Telecommunications Networking
Revealing Hidden Community Structures and Identifying Bridges in Complex Networks: An Application to Analyzing Contents of Web Pages for Browsing

WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Potential collaboration discovery using document clustering and community structure detection

Proceedings of the 1st ACM international workshop on Complex networks meet information & knowledge management
Interactive visualization and navigation of web search results revealing community structures and bridges

Proceedings of Graphics Interface 2010
Assessing group cohesion in homophily networks

Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

Quantified Score

Hi-index	0.00

Visualization

Abstract

We propose here the first model which achieves the following challenges: it produces graphs which have the three main wanted properties (clustering, degree distribution, average distance), it is based on some real-world observations, and it is sufficiently simple to make it possible to prove its main properties. This model consists in sampling a random bipartite graph with prescribed degree distribution. Indeed, we show that any can be viewed as a bipartite graph with some specific characteristics, and that its main properties can be viewed as consequences of this underlying structure.