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Mining Mechanism of Top-k Influential Nodes Based on Voting Algorithm in Mobile Social Networks

机译:移动社交网络中基于投票算法的Top-k影响节点挖掘机制

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摘要

In recent years, evaluating the influence of nodes and finding top-k influential nodes in social networks, has drawn a wide attention and has become a hot-pot research issue. Considering the characteristics of social networks, we present a novel mechanism to mine the top-k influential nodes in mobile social networks. The proposed mechanism is based on the behaviors analysis of SMS/MMS (simple messaging service / multimedia messaging service) communication between mobile users. We introduce the complex network theory to build a social relation graph, which is used to reveal the relationship among people's social contacts and messages sending. Moreover, intimacy degree is also introduced to characterize social frequency among nodes. Election mechanism is hired to find the most influential node, and then a heap sorting algorithm is used to sort the voting results to find the k most influential nodes. The experimental results show that the mechanism can finds out the most influential top-k nodes efficiently and effectively.
机译:近年来,评估节点的影响并找到社交网络中前k个有影响力的节点已引起了广泛的关注,并已成为热点研究问题。考虑到社交网络的特征,我们提出了一种新颖的机制来挖掘移动社交网络中的前k个有影响力的节点。所提出的机制基于移动用户之间的SMS / MMS(简单消息服务/多媒体消息服务)通信的行为分析。我们引入复杂的网络理论来构建社会关系图,用于揭示人们的社会联系与信息发送之间的关系。此外,还引入亲密度来表征节点之间的社交频率。利用选举机制找到最有影响力的节点,然后使用堆排序算法对投票结果进行排序以找到k个最有影响力的节点。实验结果表明,该机制可以高效,有效地找出影响最大的top-k节点。

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