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A Comparative Study of Community Structure Based Node Scores for Network Immunization

机译:基于社区结构基于网络免疫的节点分数的比较研究

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Network immunization has often been conducted by removing nodes with large network centrality so that the whole network can be fragmented into smaller subgraphs. Since contamination (e.g., virus) is propagated among subgraphs (communities) along links in a network, besides centrality, utilization of community structure seems effective for immunization. We have proposed community structure based node scores in terms of a vector representation of nodes in a network. In this paper we report a comparative study of our node scores over both synthetic and real-world networks. The characteristics of the node scores are clarified through the visualization of networks. Extensive experiments are conducted to compare the node scores with other centrality based immunization strategies. The results are encouraging and indicate that the node scores are promising.
机译:通常通过将具有大网络中心的节点移除节点来进行网络免疫,使得整个网络可以分离成较小的子图。由于涉及网络中的链路(例如,群组)在网络中的链路之间传播,除了中心性之外,社区结构的利用对于免疫似乎有效。我们在网络中的节点的矢量表示方面提出了基于社区结构的节点分数。在本文中,我们报告了对合成和现实网络的节点分数的比较研究。通过网络的可视化阐明节点分数的特征。进行广泛的实验以将节点分数与其他基于中心的免疫策略进行比较。结果令人鼓舞,表明节点得分是有前途的。

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