一种基于贡献矩阵的有向网络节点关键度计算方法

复杂网络中的关键节点，其重要程度一般要比非关键节点拥有更大影响力。目前已有的关键节点的关键度计算算法大多根据不同的衡量指标进行计算。针对适用于有向网络的关键节点挖掘算法较少且算法中不同衡量指标的结合不够严谨的情况，提出一种基于贡献矩阵的有向网络节点关键度计算算法。该算法通过贡献矩阵结合节点关联关系和节点的位置作为衡量节点关键度标准。在实验网络上的传播实验表明，相较于基于关联关系关键节点挖掘算法（RelaCentrality）来评估关键节点重要性，该算法在挖掘关键节点的过程中效率更高，并且所挖掘得到的关键节点在网络中对信息的传播更为广泛。

An Algorithm for Mining Key Nodes of Directed Networks Based on Contribution Matrix

Critical nodes in complex networks are generally more important than non-critical nodes. Existing methods to calculate the importance of nodes are mostly based on different measurement criteria. At present, in the case of less calculation methods for node criticality applicable to directed networks and less rigorous combination of different measurement indexes in the methods, a new method for calculating node criticality of directed network is proposed. The algorithm calculates the node key value by combining the node relation and the node position with the contribution matrix. The propagation experiment on the experimental network shows that, compared with the classical algorithm that evaluates the importance of key nodes by node degree centrality and other methods, this algorithm is more efficient in the process of mining key nodes, and the mined key nodes spread information more widely in the network.