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Identifying Key Nodes of Network Based on Subjective-Objective Weighting Method for Structural Holes

机译:基于主观目标加权的结构孔网络关键节点识别

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It is of great significance to identify the key nodes of complex networks in practical applications. For the key node of single index recognition network has a strong one-sidedness, the method of the key node of multiple index recognition network can evaluate the importance of nodes comprehensively, but it doesn't take into account the principle of structure hole, so the importance of the node in the structure hole cannot be evaluated accurately. In addition, when considering the weighting of measures, only the subjective or objective factors are considered. This paper presents a method to identify the key nodes of the network based on subjective-objective weighting method for structural holes, when identifying the key nodes of the network, the method not only combines many indexes of the key nodes with the theory of structural holes, but also combines the analytic hierarchy process (subjective weighting method) and the information entropy method (objective weighting method) to empower the indexes. This method not only overcomes the one-sidedness of the key nodes of single index recognition complex network, but also overcomes the shortcoming of single weighting method, and can accurately evaluate the importance of the node in the structure hole. Experiments were carried out on three actual complex networks, the experimental results show that the method can identify the key nodes of the network accurately, and simulate the network cascading fault in a practical complex network, and verify that the method can achieve more number of subgraphs and smaller scale ratio of maximal connected subgraph.
机译:在实际应用中,确定复杂网络的关键节点具有重要意义。由于单索引识别网络的关键节点具有很强的一面性,因此多索引识别网络的关键节点的方法可以全面评估节点的重要性,但没有考虑结构孔的原理,因此无法准确评估结点在结构孔中的重要性。另外,在考虑度量的权重时,仅考虑主观或客观因素。本文提出了一种基于主观客观加权的结构孔识别网络关键节点的方法,当识别网络的关键节点时,该方法不仅将关键节点的多个指标与结构孔理论相结合。 ,而且还结合了层次分析法(主观加权法)和信息熵方法(客观加权法)来赋权指标。该方法不仅克服了单一索引识别复杂网络关键节点的单一性,而且克服了单一加权方法的弊端,可以准确地评估节点在结构孔中的重要性。在三个实际的复杂网络上进行了实验,实验结果表明,该方法能够准确识别出网络的关键节点,并在实际的复杂网络中模拟网络级联故障,证明该方法可以实现更多的子图。和最大连通子图的较小比例比例。

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