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Identifying Influential Nodes in Online Social Networks Using Principal Component Centrality

机译:使用主成分中心标识在线社交网络中的有影响性节点

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Identifying the most influential nodes in social networks 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.
机译:识别社交网络中最有影响力的节点是社交网络分析中的关键问题。但是,在没有严格定义中心的定义,在网络中构成中央节点的概念随着应用程序和流经网络流经的商品类型。在本文中,我们使用主成分中心(PCC)在大规模的在线社交网络中识别社交集线器,在有影响力的社区中心的节点。我们将PCC与Eigenvector Centrality(EVC)进行比较,通过网络在网络中的职位来对节点影响的事实衡量。我们通过处理谷歌的Orkut社交网络服务的70,000名用户的友谊图以及143,020用户获得的游戏图来展示PCC的表现,以及从Facebook的“战士俱乐部”应用程序的用户提供的143,020名用户。

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