Efficient Implementation of Anchored 2-core Algorithm

dc.contributor.authorTootoonchi, Babak
dc.contributor.supervisorThomo, Alex
dc.date.accessioned2017-04-28T22:06:23Z
dc.date.available2017-04-28T22:06:23Z
dc.date.copyright2017en_US
dc.date.issued2017-04-28
dc.degree.departmentDepartment of Computer Science
dc.degree.levelMaster of Science M.Sc.en_US
dc.description.abstractOften graph theory is used to model and analyze different behaviors of networks including social networks. Nowadays, social networks have become very popular and social network providers try to expand their networks by encouraging people to stay engaged and active. Studies show that engagement and activities of people in social networks influence engagement of their connections. This behavior has been modeled by the k-core problem in graph theory assuming that a person stays active in the network if he or she has k or more connections. In the above model if a person drops out, his or her friends can become discouraged and they might also drop out. An approach called anchored k-core algorithm has been introduced lately that prevents a cascade of drop-outs by finding nodes which have the most influence on their connections and rewarding them to stay in the network. In this work, an efficient implementation of the anchored 2-core approach has been proposed. The proposed implementation method was applied on a set of real world network data that includes very large networks with millions of links. The results show that with only a few anchors, it is possible to save hundreds of nodes for the 2-core graph. Also, the execution time of our implementation is in order of minutes for larger datasets that proves the efficiency of our implementation.en_US
dc.description.scholarlevelGraduateen_US
dc.identifier.urihttp://hdl.handle.net/1828/8008
dc.language.isoenen_US
dc.rightsAvailable to the World Wide Weben_US
dc.subjectAnchored K-coreen_US
dc.subjectSocial Networksen_US
dc.titleEfficient Implementation of Anchored 2-core Algorithmen_US
dc.typeprojecten_US

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