Identification of influential nodes in social networks with community structure based on label propagation
Identification of influential nodes in social networks with community structure based on label propagation
复制标题
基于标签传播的社区结构社交网络中影响力节点识别
DOI：
10.1016/j.neucom.2015.11.125
复制
发表时间：
2016-10
期刊：
影响因子：
6
通讯作者：
Jin Feng
中科院分区：
文献类型：
--
作者：
Zhao Yuxin;Li Shenghong;Jin Feng
社会网络是社会系统的一种抽象表示，其中思想和信息通过个体之间的交互进行传播。如何在社会网络中找到一组最具影响力的个体，使他们能够在网络中最大范围地传播影响力，是一个至关重要的问题。传统的识别网络中有影响力节点的方法是基于贪婪算法或特定的中心性度量。社区结构是社交网络的一个重要的拓扑性质，近年来的研究表明，社区结构对网络的动态行为有着重要的影响。然而，大多数影响力最大化方法没有考虑网络中的社团结构，这限制了它们在具有社团结构的社交网络中的应用。本文提出了一种基于标签传播的社区结构社交网络中有影响力节点识别算法。该算法通过标签传播过程，发现网络中不同社区的核心节点。此外，我们的算法具有较低的时间复杂度，这使得它适用于大规模的网络。在常见的扩散模型下，对合成网络和真实网络进行了大量的实验，证明了该算法的有效性和效率。
Social network is an abstract presentation of social systems where ideas and information propagate through the interactions between individuals. It is an essential issue to find a set of most influential individuals in a social network so that they can spread influence to the largest range on the network. Traditional methods for identifying influential nodes in networks are based on greedy algorithm or specific centrality measures. Some recent researches have shown that community structure, which is a common and important topological property of social networks, has significant effect on the dynamics of networks. However, most influence maximization methods do not take into consideration the community structure in the network, which limits their applications on social networks with community structure. In this paper, we propose a new algorithm for identifying influential nodes in social networks with community structure based on label propagation. The proposed algorithm can find the core nodes of different communities in the network through the label propagation process. Moreover, our algorithm has low time complexity, which makes it applicable to large-scale networks. Extensive experiments on both synthetic and real-world networks under common diffusion models demonstrate the effectiveness and efficiency of our proposed algorithm.
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DOI：
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发表时间：
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期刊：
--
影响因子：
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