Proceedings ArticleDOI
Mining System for Community Finding and Centrality of Virtual User Network on the Internet
Sen Qin,Guan-zhong Dai,Yan-ling Li +2 more
- pp 284-289
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TLDR
The topology of virtual user network is analyzed firstly, and it shows that the network has a scale-free characteristic and the straightforward and effective criterion evaluating the correctness to detect the community structure has been established.Abstract:
Community finding of virtual user network on the Internet has provoked more and more researchers? interests in the field of computer and physics field recently. Existed results focus on how to seek a reasonable and feasible algorithm to find the communities of virtual user network mostly, but scarcely relate to the centrality of those found communities. In addition, the centrality of those found communities is usually not involved. In this paper, the topology of virtual user network is analyzed firstly, and it shows that the network has a scale-free characteristic. The straightforward and effective criterion evaluating the correctness to detect the community structure has been established. Then the mining system for community finding and centrality of the network is designed and its workflow is presented. Two key modules of this system, community finding module and centrality module, are studied in detail. An implementation example is given to verify the validity of the design. Finally, the paper is summarized.read more
Citations
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Journal ArticleDOI
Betweenness Centrality in Large Complex Networks
TL;DR: In this article, the authors analyzed the betweenness centrality of nodes in large complex networks and showed that for trees or networks with a small loop density, a larger density of loops leads to the same result.
Book ChapterDOI
Community Finding of Scale-Free Network: Algorithm and Evaluation Criterion
Sen Qin,Guanzhong Dai +1 more
TL;DR: Numerical results show that the algorithm of community finding is an effective one and the evaluation criterion is feasible, fast and easy to operate.
Proceedings ArticleDOI
Evaluation Method of Node Centrality in Complex Directed Network
TL;DR: A new method for measuring the node centrality of complex directed network is provided, which considers the node's role in network efficiency and degree distribution and the importance of adjacent edges to make the evaluation of nodecentrality more precisely.
References
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Journal ArticleDOI
Emergence of Scaling in Random Networks
TL;DR: A model based on these two ingredients reproduces the observed stationary scale-free distributions, which indicates that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.
Journal ArticleDOI
The Structure and Function of Complex Networks
TL;DR: Developments in this field are reviewed, including such concepts as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks.
Journal ArticleDOI
Centrality in social networks conceptual clarification
TL;DR: In this article, three distinct intuitive notions of centrality are uncovered and existing measures are refined to embody these conceptions, and the implications of these measures for the experimental study of small groups are examined.
Journal ArticleDOI
Finding and evaluating community structure in networks.
TL;DR: It is demonstrated that the algorithms proposed are highly effective at discovering community structure in both computer-generated and real-world network data, and can be used to shed light on the sometimes dauntingly complex structure of networked systems.
Journal ArticleDOI
Mean-field theory for scale-free random networks
TL;DR: A mean-field method is developed to predict the growth dynamics of the individual vertices of the scale-free model, and this is used to calculate analytically the connectivity distribution and the scaling exponents.