中文摘要：

节点重要性度量对于研究复杂网络鲁棒性与脆弱性具有重要意义.大规模实际复杂网络的结构往往随着时间不断变化,在获取网络全局信息用于评估节点重要性方面具有局限性.通过量化节点局部网络拓扑的重合程度来定义节点间的相似性,提出了一种考虑节点度以及邻居节点拓扑重合度的节点重要性评估算法,算法只需要获取节点两跳内的邻居节点信息,通过计算邻居节点对之间的相似度,便可表征其在复杂网络中的结构重要性.基于六个经典的实际网络和一个人工的小世界网络,分别以静态与动态的方式对网络进行攻击,通过对极大连通系数与网络效率两种评估指标的实验结果对比,证明了所提算法优于基于局域信息的度指标、半局部度指标、基于节点度及其邻居度的WL指标以及基于节点位置的K-shell指标.

英文摘要：

Ranking node importance is of great significance for studying the robustness and vulnerability of complex network.Over the recent years, various centrality indices such as degree, semilocal, K-shell, betweenness and closeness centrality have been employed to measure node importance in the network. Among them, some well-known global measures such as betweenness centrality and closeness centrality can achieve generally higher accuracy in ranking nodes, while their computation complexity is relatively high, and also the global information is not readily available in a large-scaled network. In this paper, we propose a new local metric which only needs to obtain the neighborhood information within two hops of the node to rank node importance. Firstly, we calculate the similarity of node neighbors by quantifying the overlap of their topological structures with Jaccard index; secondly, the similarity between pairs of neighbor nodes is calculated synthetically, and the redundancy of the local link of nodes is obtained. Finally, by reducing the influence of densely local links on ranking node importance, a new local index named LLS that considers both neighborhood similarity and node degree is proposed. To check the effectiveness of the proposed method of ranking node importance,we carry out it on six real world networks and one artificial small-world network by static attacks and dynamic attacks.In the static attack mode, the ranking value of each node is the same as that in the original network. In the dynamic attack mode, once the nodes are removed, the centrality of each node needs recalculating. The relative size of the giant component and the network efficiency are used for network connectivity assessment during the attack. A faster decrease in the size of the giant component and a faster decay of network efficiency indicate a more effective attack strategy.By comparing the decline rates of these two indices to evaluate the connectedness of all networks, we find that the proposed method is more efficient than tradit