International Conference
Permanent URI for this collectionhttps://dspace.psgrkcw.com/handle/123456789/177
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Item COMMUNITY DETECTION BASED ON GIRVAN NEWMAN ALGORITHM AND LINK ANALYSIS OF SOCIAL MEDIA(P S G R Krishnammal College for Women, 2016-11) Sathiyakumari K; Vijaya M SSocial networks have acquired much attention recently, largely due to the success of online social networking sites and media sharing sites. In such networks, rigorous and complex interactions occur among numerous one-of-a-kind entities, main to massive statistics networks with notable enterprise capacity. Community detection is an unsupervised learning task that determines the community groups based on common interests, occupation, modules and their hierarchical organization, using the information encoded in the graph topology. Finding communities from the social network is a difficult task because of its topology and overlapping of different communities. In this research, the Girvan-Newman algorithm based on Edge-Betweenness Modularity and Link Analysis (EBMLA) is used for detecting communities in networks with node attributes. The twitter data of the well-known cricket player is used right here and community of friends and fans is analyzed based on three exclusive centrality measures together with a degree, betweenness, and closeness centrality. Also, the strength of extracted communities is evaluated based on modularity score using proposed method and the experiment results confirmed that the cricket player’s network is dense.Item IDENTIFICATION OF SUBGROUPS IN A DIRECTED SOCIAL NETWORK USING EDGE BETWEENNESS AND RANDOM WALKS(PSGR Krishnammal College for Women, 2017-12) Sathiyakumari K; Vijaya M SSocial networks have obtained masses hobby recently, largely because of the success of online social networking Web sites and media sharing sites. In such networks, rigorous and complex interactions occur among several unique entities, leading to huge information networks with first rate commercial enterprise ability. Network detection is an unmanaged getting to know challenge that determines the community groups based on common place hobbies, career, modules, and their hierarchical agency, the usage of the records encoded in the graph topology. Locating groups from social network is a tough mission because of its topology and overlapping of various communities. In this research, edge betweenness modularity and random walks is used for detecting groups in networks with node attributes. The twitter data of the famous cricket player is used here and network of friends and followers is analyzed using two algorithms based on edge betweenness and random walks. Also the strength of extracted communities is evaluated using on modularity score and the experiment results confirmed that the cricket player’s network is dense.