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Influential node ranking in social networks based on neighborhood diversity

机译:基于邻域多样性的社交网络中有影响力的节点排名

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摘要

Social networks have significant role in distribution of ideas and advertisement. Discovering the most influential nodes has been a hot topic in the field of social networks analysis and mining. This manuscript proposes novel algorithms for this purpose based on neighborhood diversity. We introduce two new influential node ranking algorithms that use diversity of the neighbors of each node in order to obtain its ranking value. They are applied on a number of real-world networks and compared with state-of-the-art algorithms. Our experimental results reveal effectiveness of the proposed algorithms. (C) 2018 Elsevier B.V. All rights reserved.
机译:社交网络在传播思想和广告方面起着重要作用。发现最具影响力的节点一直是社交网络分析和挖掘领域的热门话题。该手稿为此基于邻域分集提出了新颖的算法。我们介绍了两种新的有影响力的节点排名算法,这些算法使用每个节点的邻居的多样性来获得其排名值。它们被应用在许多真实世界的网络上,并与最新算法进行了比较。我们的实验结果揭示了所提出算法的有效性。 (C)2018 Elsevier B.V.保留所有权利。

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