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Identifying Influential Nodes in Online Social Networks Using Principal Component Centrality

Identifying Influential Nodes in Online Social Networks Using Principal Component Centrality,10.1109/icc.2011.5963147,Muhammad U. Ilyas,Hayder Radha

Identifying Influential Nodes in Online Social Networks Using Principal Component Centrality  
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Identifying the most influential nodes in social net- works is a key problem in social network analysis. However, without a strict definition of centrality the notion of what constitutes a central node in a network changes with application and the type of commodity flowing through a network. In this paper we identify social hubs, nodes at the center of influential neighborhoods, in massive online social networks using principal component centrality (PCC). We compare PCC with eigenvector centrality's (EVC), the de facto measure of node influence by virtue of their position in a network. We demonstrate PCC's performance by processing a friendship graph of 70,000 users of Google's Orkut social networking service and a gaming graph of 143, 020 users obtained from users of Facebook's 'Fighters' Club' application.
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