KiC:一种结合“结构洞”约束值与K壳分解的社交网络关键节点识别算法

目的/意义] 在大规模社交网络中快速搜索关键节点对于舆情的引导和控制具有重要意义。方法/过程] 本文提出一种适用于社交网络的局部中心性关键节点识别算法，该方法综合评估了节点的K壳、自身的聚集特性以及邻居的扩散特性和节点自身传播状态，同时体现了节点在空间上的网络位置和邻居的拓扑结构以及在时间上演化特征，评价指标更加全面高效。结果/结论] 实验结果表明，该方法识别的关键节点对网络鲁棒性的影响与介数中心性接近，但计算仅基于节点局部信息，时间复杂度低。剔除这些节点后网络的连通性受到较大影响，网络聚类系数降低，平均路径长度增加。同时，利用SIR传播模型模拟验证，以该算法识别的关键节点为初始传播源可提升信息传播范围和平均传播速度。

KiC:An Extended K-shell Decomposition Based on Improved Network Constraint Coefficient

Purpose/Significance] Evaluating vital nodes rapidly in large-scale social networks is of great significance for the control of information dissemination.Method/Process] In this paper,we proposed a local centrality vital node identification algorithm.The method comprehensively evaluated the K-shell of a node,its own clustering characteristics,the diffusion characteristics of its neighbors and propagation state of nodes,which simultaneously reflected the network location of the nodes,the topology of the neighbors and evolutionary features in time.The evaluation indicators were more comprehensive and efficient.Result/Conclusion] The experimental results showed that the vital nodes identified by this method had a greater impact on the robustness of the network.After removing these nodes,the connectivity of the network was greatly affected,the network clustering coefficient was reduced,and the average path length was increased.Meanwhile,SIR model was used to evaluate the ability to spread nodes.Simulations of five real networks showed that our proposed method could improve the scope and average speed of information dissemination.