{"title":"利用间度中心性对一维周期性网络进行聚类。","authors":"Norie Fu, Vorapong Suppakitpaisarn","doi":"10.1186/s40649-016-0031-1","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>While the temporal networks have a wide range of applications such as opportunistic communication, there are not many clustering algorithms specifically proposed for them.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>The algorithm provides a better result for temporal social networks with an acceptable running time.</p>","PeriodicalId":52145,"journal":{"name":"Computational Social Networks","volume":"3 1","pages":"6"},"PeriodicalIF":0.0000,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5749596/pdf/","citationCount":"0","resultStr":"{\"title\":\"Clustering 1-dimensional periodic network using betweenness centrality.\",\"authors\":\"Norie Fu, Vorapong Suppakitpaisarn\",\"doi\":\"10.1186/s40649-016-0031-1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>While the temporal networks have a wide range of applications such as opportunistic communication, there are not many clustering algorithms specifically proposed for them.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>The algorithm provides a better result for temporal social networks with an acceptable running time.</p>\",\"PeriodicalId\":52145,\"journal\":{\"name\":\"Computational Social Networks\",\"volume\":\"3 1\",\"pages\":\"6\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5749596/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computational Social Networks\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1186/s40649-016-0031-1\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2016/10/21 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q1\",\"JCRName\":\"Mathematics\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computational Social Networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1186/s40649-016-0031-1","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2016/10/21 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"Mathematics","Score":null,"Total":0}
Clustering 1-dimensional periodic network using betweenness centrality.
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.
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.
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