Efficient Implementation of Anchored 2-core Algorithm

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dc.contributor.author Tootoonchi, Babak
dc.date.accessioned 2017-04-28T22:06:23Z
dc.date.available 2017-04-28T22:06:23Z
dc.date.copyright 2017 en_US
dc.date.issued 2017-04-28
dc.identifier.uri http://hdl.handle.net/1828/8008
dc.description.abstract Often 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.language.iso en en_US
dc.rights Available to the World Wide Web en_US
dc.subject Anchored K-core en_US
dc.subject Social Networks en_US
dc.title Efficient Implementation of Anchored 2-core Algorithm en_US
dc.type project en_US
dc.contributor.supervisor Thomo, Alex
dc.degree.department Department of Computer Science en_US
dc.degree.level Master of Science M.Sc. en_US
dc.description.scholarlevel Graduate en_US

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