Corpus ID: 18771352

Basic Notions for the Analysis of Large Affiliation Networks / Bipartite Graphs

  title={Basic Notions for the Analysis of Large Affiliation Networks / Bipartite Graphs},
  author={Matthieu Latapy and Cl{\'e}mence Magnien and Nathalie Del Vecchio Liafa - Cnrs and 7 UniversiteParis and Crea - Cnrs and Ecole Nationale Polytechnique and 2 LARGEPA-UniversiteParis},
  journal={arXiv: Statistical Mechanics},
Many real-world complex networks actually have a bipartite nature: their nodes may be separated into two classes, the links being between nodes of different classes only. Despite this, and despite the fact that many ad-hoc tools have been designed for the study of special cases, very few exist to analyse (describe, extract relevant information) such networks in a systematic way. We propose here an extension of the most basic notions used nowadays to analyse classical complex networks to the… Expand
Theoretical Computer Science special issue on Complex Networks
Complex networks are large networks encountered in practice that seem to lack any apparent structure. Typical complex networks include internet topologies, web graphs, peer-to-peer networks, socialExpand
Clustering coefficient and community structure of bipartite networks
Many real-world networks display natural bipartite structure, where the basic cycle is a square. In this paper, with the similar consideration of standard clustering coefficient in binary networks, aExpand
Comparative Definition of Community in Bipartite Network
In order to know the community structure in bipartite network,we propose a comparative definition of community in bipartite network,overlapping between communities is allowed in this definition.WeExpand
Modeling online social networks using Quasi-clique communities
A random graph model that generates social networks using a community-based approach, in which users’ affiliations to communities are explicitly modeled and then translated into a social network, is proposed, which is the first community- based model that can accurately reproduce a variety of important social network characteristics. Expand
Social network of the competing crowd
  • Kai Lu, Wenjun Zhou, Xuehua Wang
  • Computer Science
  • 2014 International Conference on Behavioral, Economic, and Socio-Cultural Computing (BESC2014)
  • 2014
This paper analyzes the social network among competition participants at, finding a large number of connected components in these networks, which shows that players at this marketplace are densely but not widely connected. Expand
The structure analysis of user behaviors for web traffic
  • Cai Jun, Yu Shun-zheng
  • Computer Science
  • 2009 ISECS International Colloquium on Computing, Communication, Control, and Management
  • 2009
The algorithm of finding the community structure in bipartite network divided the clients into different interest communities and obtained the result that the out-degree distribution of clients, the in- degree distribution of servers and the strength distribution is power-law. Expand
The community Analysis of User Behaviors Network for Web Traffic
The structure analysis of UBNWT is very helpful for the network management, resource allocation, traffic engineering andSecurity, and the loyalty of clients belonging to the same community in different time is higher than 80%. Expand
An Alternative Approach to the Calculation and Analysis of Connectivity in the World City Network
An approach in which network centrality measures are interpreted against a randomized baseline model that retains the network's original degree distribution is introduced, which is applied to Taylor's specification of world cities being ‘interlocked’ through the office networks of globalized service firms. Expand
Piecewise-Linear Distance-Dependent Random Graph Models
In this paper, we propose a form of random graph (network) model in which the probability of an edge (link) is dependent on a real-valued function on pairs of vertices (nodes). In general, we expectExpand
Analysing the Use of Ontologies Based on Usage Network
  • Jamshaid Ashraf, O. Hussain
  • Computer Science
  • 2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology
  • 2012
To analyse the usage of ontologies by data publishers, the Ontology Usage Network is model, which is a bipartite affiliation network, is used to study statistical and structural properties such as degree distribution, centrality measures and identification of cohesive subgroups. Expand


Bipartite structure of all complex networks
This work proposes the first simple and intuitive model for complex networks which captures the main properties met in practice, and shows here that all complex networks can be viewed as bipartite structures sharing some important statistics, like degree distributions. Expand
Bipartite graphs as models of complex networks
It appeared recently that the classical random graph model used to represent real-world complex networks does not capture their main properties. Since then, various attempts have been made to provideExpand
Analysis of weighted networks.
  • M. Newman
  • Mathematics, Physics
  • Physical review. E, Statistical, nonlinear, and soft matter physics
  • 2004
It is pointed out that weighted networks can in many cases be analyzed using a simple mapping from a weighted network to an unweighted multigraph, allowing us to apply standard techniques for unweighting graphs to weighted ones as well. Expand
The architecture of complex weighted networks.
This work studies the scientific collaboration network and the world-wide air-transportation network, which are representative examples of social and large infrastructure systems, respectively, and defines appropriate metrics combining weighted and topological observables that enable it to characterize the complex statistical properties and heterogeneity of the actual strength of edges and vertices. Expand
Random graph models of social networks
  • M. Newman, D. Watts, S. Strogatz
  • Computer Science, Medicine
  • Proceedings of the National Academy of Sciences of the United States of America
  • 2002
It is found that in some cases, the models are in remarkable agreement with the data, whereas in others the agreement is poorer, perhaps indicating the presence of additional social structure in the network that is not captured by the random graph. Expand
Cycles and clustering in bipartite networks.
An expression is deduced for estimating cycles of larger size, which improves previous estimations and is suitable for either monopartite and multipartite networks, and the applicability of such analytical estimations is discussed. Expand
Characterization and modeling of weighted networks
The main empirical results are the broad distributions of various quantities and the existence of weight-topology correlations which show that weights are relevant and that in general the modeling of complex networks must go beyond topology. Expand
Correlations in bipartite collaboration networks
Collaboration networks are studied as an example of growing bipartite networks. These have been previously observed to exhibit structure such as positive correlations between nearest-neighbourExpand
Random graphs with arbitrary degree distributions and their applications.
It is demonstrated that in some cases random graphs with appropriate distributions of vertex degree predict with surprising accuracy the behavior of the real world, while in others there is a measurable discrepancy between theory and reality, perhaps indicating the presence of additional social structure in the network that is not captured by the random graph. Expand
Handbook of Graphs and Networks: From the Genome to the Internet
This book defines the field of complex interacting networks in its infancy and presents the dynamics of networks and their structure as a key concept across disciplines and offers concepts to model network structures and dynamics, focussed on approaches applicable across disciplines. Expand