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Establishing Connections in a Social Network Radial Versus Medial Centrality Indices

机译:建立社交网络径向与内侧中心指数的连接

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The extent to which a node occupies a central position in a social network amounts to a crucial indicator of personal influence. Few works address the mechanisms that would allow an individual to integrate into a network, and even fewer examine the correlation between this process and various notions of centrality. In this paper, we tackle this problem by focusing on the process in which a newcomer joins a network through building connections and gains centrality. We provide three efficient heuristics that build edges between the newcomer and existing members of a social network and compare their performances in terms of three centrality metrics. We perform experiments on random graphs generated by two synthetic network models and four real-world networks. Not only our heuristics considerably outperform the random benchmark algorithm, but the results also reveal some new insights that are related to centrality and network topology. In particular, the results distinguish between measures along two dimensions: the first concerns with the number of centers in a network, and the second concerns with the type of involvement of a node in the network.
机译:节点在社交网络中占据中心位置的程度相当于个人影响的重要指标。很少有效地解决了允许个人集成到网络中的机制,甚至更少地检查该过程与中心性的各种概念之间的相关性。在本文中,我们通过专注于新人通过建立连接和增长中心地加入网络的过程来解决这个问题。我们提供三种高效的启发式信息,在新人和社会网络的现有成员之间建立边缘,并在三个中心度量方面进行比较他们的表现。我们对两个合成网络模型和四个真实网络产生的随机图进行实验。不仅我们的启发式大致优于随机基准算法,而且结果还揭示了与中心和网络拓扑相关的一些新见解。特别地,结果区分了沿两个维度的措施:与网络中的中心数量的第一个问题,以及网络中节点参与的第二个问题。

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