Clustering 1-dimensional periodic network using betweenness centrality.

Q1 Mathematics

Computational Social Networks Pub Date : 2016-01-01 Epub Date: 2016-10-21 DOI:10.1186/s40649-016-0031-1

Norie Fu, Vorapong Suppakitpaisarn

引用次数: 0

Abstract

Background: While the temporal networks have a wide range of applications such as opportunistic communication, there are not many clustering algorithms specifically proposed for them.

Methods: Based on betweenness centrality for periodic graphs, we give a clustering pseudo-polynomial time algorithm for temporal networks, in which the transit value is always positive and the least common multiple of all transit values is bounded.

Results: Our experimental results show that the centrality of networks with 125 nodes and 455 edges can be efficiently computed in 3.2 s. Not only the clustering results using the infinite betweenness centrality for this kind of networks are better, but also the nodes with biggest influences are more precisely detected when the betweenness centrality is computed over the periodic graph.

Conclusion: The algorithm provides a better result for temporal social networks with an acceptable running time.

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利用间度中心性对一维周期性网络进行聚类。

背景：虽然时态网络在机会通信等方面有着广泛的应用，但专门针对时态网络提出的聚类算法并不多：基于周期图的间度中心性，我们给出了时态网络的聚类伪多项式时间算法，其中中转值总是正的，且所有中转值的最小公倍数是有界的：我们的实验结果表明，可以在 3.2 秒内高效计算 125 个节点和 455 条边的网络的中心度。对这类网络使用无限中心度不仅聚类结果更好，而且在周期图上计算中心度时，可以更精确地检测出影响最大的节点：结论：该算法能在可接受的运行时间内为时间性社交网络提供更好的结果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Computational Social Networks Mathematics-Modeling and Simulation

自引率

0.00%

发文量

审稿时长

13 weeks

期刊介绍： Computational Social Networks showcases refereed papers dealing with all mathematical, computational and applied aspects of social computing. The objective of this journal is to advance and promote the theoretical foundation, mathematical aspects, and applications of social computing. Submissions are welcome which focus on common principles, algorithms and tools that govern network structures/topologies, network functionalities, security and privacy, network behaviors, information diffusions and influence, social recommendation systems which are applicable to all types of social networks and social media. Topics include (but are not limited to) the following: -Social network design and architecture -Mathematical modeling and analysis -Real-world complex networks -Information retrieval in social contexts, political analysts -Network structure analysis -Network dynamics optimization -Complex network robustness and vulnerability -Information diffusion models and analysis -Security and privacy -Searching in complex networks -Efficient algorithms -Network behaviors -Trust and reputation -Social Influence -Social Recommendation -Social media analysis -Big data analysis on online social networks This journal publishes rigorously refereed papers dealing with all mathematical, computational and applied aspects of social computing. The journal also includes reviews of appropriate books as special issues on hot topics.