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首页> 外文会议>Pacific Rim international conference on artificial intelligence >Establishing Connections in a Social Network: Radial Versus Medial Centrality Indices
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Establishing Connections in a Social Network: Radial Versus Medial Centrality Indices

机译：在社交网络中建立联系：径向对中枢指数

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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等于个人影响力的关键指标。很少有著作探讨允许个人集成到网络中的机制，甚至很少有人研究此过程与各种中心性概念之间的相关性。在本文中，我们将重点放在新手通过建立连接加入网络并获得中心地位的过程中，以解决此问题。我们提供了三种有效的启发式方法，它们在社交网络的新手和现有成员之间建立了优势，并根据三个中心度指标比较了他们的表现。我们对由两个合成网络模型和四个真实网络生成的随机图进行实验。我们的试探法不仅大大优于随机基准算法，而且结果还揭示了一些与中心性和网络拓扑有关的新见解。尤其是，结果在两个维度上对度量进行了区分：第一个与网络中中心的数量有关，第二个与网络中节点的参与类型有关。

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