Topological features of online social networks

dc.contributor.authorSridharan, Ajay Promodh
dc.contributor.supervisorWu, Kui
dc.contributor.supervisorGao, Yong
dc.date.accessioned2011-07-05T20:33:52Z
dc.date.available2011-07-05T20:33:52Z
dc.date.copyright2011en_US
dc.date.issued2011-07-05
dc.degree.departmentDepartment of Computer Science
dc.degree.levelMaster of Science M.Sc.en_US
dc.description.abstractThe first-order properties like degree distribution of nodes and the clustering co-efficient have been the prime focus of research in the study of structural properties of networks. The presence of a power law in the degree distribution of nodes has been considered as an important structural characteristic of social and information networks. Higher-order structural properties such as edge embeddedness may also play a more important role in many on-line social networks but have not been studied before. In this research, we study the distribution of higher-order structural properties of a network, such as edge embeddedness, in complex network models and on-line social networks. We empirically study the embeddedness distribution of a variety of network models and theoretically prove that a recently-proposed network model, the random $k$-tree, has a power-law embedded distribution. We conduct extensive experiments on the embeddedness distribution in real-world networks and provide evidence on the correlation between embeddedeness and communication patterns among the members in an on-line social network.en_US
dc.description.scholarlevelGraduateen_US
dc.identifier.bibliographicCitationAjay Sridharan and Yong Gao and Kui Wu and James Nastos, “ Statistical Behavior of Embeddedness and Communities of Overlapping Cliques in Online Social Networks”, 2011 Proceedings IEEE INFOCOM (INFOCOM 2011), pp. 546-550, April 2011en_US
dc.identifier.urihttp://hdl.handle.net/1828/3396
dc.languageEnglisheng
dc.language.isoenen_US
dc.rights.tempAvailable to the World Wide Weben_US
dc.subjectembeddednessen_US
dc.subjectpower lawen_US
dc.subjectedge embeddednessen_US
dc.subjectembeddedness distributionen_US
dc.subjectonline social networksen_US
dc.titleTopological features of online social networksen_US
dc.typeThesisen_US

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