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Effects of Inoculation Based on Structural Centrality on Rumor Dynamics in Social Networks

机译:基于结构中心的接种对社交网络谣言动力学的影响

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In social networks, the mechanism to suppress harmful rumors is of great importance. A rumor spreading model has been defined using the susceptible-infected-refractory (SIR) model to characterize rumor propagation in social networks. In this paper a new inoculation strategy based on structural centrality has been applied on rumor spreading model for heterogeneous networks. It is compared with the targeted and random inoculations. The structural centrality of each nodes has been ranked in the topology of social networks which is modeled as scale free network. The nodes with higher structural centrality are chosen for inoculation in the proposed strategy. The structural centrality based inoculation strategy is more efficient in comparison with the random and targeted inoculation strategies. One of the bottlenecks is the high complexity to calculate the structural centrality of the nodes for very large number of nodes in the complex networks. The proposed hypothesis has been verified using simulation results for email network data and the generated scale free networks.
机译:在社交网络中,抑制有害谣言的机制非常重要。使用易感感染难治性(SIR)模型定义了谣言传播模型,以表征社交网络中的谣言传播。本文在异构网络的谣言传播模型中采用了一种基于结构中心的新接种策略。将其与目标接种和随机接种进行比较。每个节点的结构中心已经在建模为无标度网络的社交网络拓扑中排名。在所提出的策略中,选择具有较高结构中心性的节点进行接种。与随机和有针对性的接种策略相比,基于结构中心的接种策略效率更高。瓶颈之一是对于复杂网络中非常大量的节点而言,计算节点的结构中心点的复杂性很高。使用电子邮件网络数据和生成的无标度网络的仿真结果,已经验证了所提出的假设。

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