Identifying influential nodes in complex networks is still an open issue. In this paper, a new comprehensive centrality mea- sure is proposed based on the Dempster-Shafer evidence theory. The existing measures of degr...Identifying influential nodes in complex networks is still an open issue. In this paper, a new comprehensive centrality mea- sure is proposed based on the Dempster-Shafer evidence theory. The existing measures of degree centrality, betweenness centra- lity and closeness centrality are taken into consideration in the proposed method. Numerical examples are used to illustrate the effectiveness of the proposed method.展开更多
In order to assess influential nodes in complex networks, the authors propose a novel ranking method based on structural hole in combination with the degree ratio of a node and its neighbors. The proposed method is a ...In order to assess influential nodes in complex networks, the authors propose a novel ranking method based on structural hole in combination with the degree ratio of a node and its neighbors. The proposed method is a response to the limitations of other proposed measures in this field. The structural hole gives a comprehensive attention of the information about the node topology in relation to its neighbors, whereas the degree ratio of nodes reflects its significance against the neighbors.Combination of the two aforementioned measures summarized in the structural hole leverage matrix demonstrates the importance of a node according to its position in the network structure. So a more accurate method for ranking influential nodes is established. The simulation results over different-scale networks(small networks with less than 30 nodes, medium networks with less than 150 nodes and large networks with more than 1000 nodes) suggest that the proposed method can rank important nodes more effectively and precisely in complex networks specifically in larger ones.展开更多
The identification of the influential nodes in a network is of great significance for understanding the features of the network and controlling the complexity of networks in society and in biology. In this paper, we ...The identification of the influential nodes in a network is of great significance for understanding the features of the network and controlling the complexity of networks in society and in biology. In this paper, we propose a novel centrality measure for a node by considering the importance of edges and compare the performance of this method with existing seven topological-based ranking methods on the Susceptible-Infected-Recovered （SIR） model. The simulation results for four different types of real networks show that the proposed method is robust and exhibits excellent performance in identifying the most influential nodes when spreading starting from both single origin and multipleorigins simultaneously.展开更多
准确高效地发现网络中有影响力的传播者具有非常重要的理论和现实意义。近年来,结点影响力排序受到了多领域学者的广泛关注。K-shell是一种较好的结点影响力评价指标;然而,仅仅依赖结点自身K-shell值实现的算法通常具有评估结果精确度...准确高效地发现网络中有影响力的传播者具有非常重要的理论和现实意义。近年来,结点影响力排序受到了多领域学者的广泛关注。K-shell是一种较好的结点影响力评价指标;然而,仅仅依赖结点自身K-shell值实现的算法通常具有评估结果精确度不高、适用性较差等缺陷。针对此问题,提出KSN(the K-shell and neighborhood centrality)中心性模型,该算法综合考虑了结点本身及其所有二阶以内邻居结点的K-shell值。实验结果表明,所提出算法度量结点传播的能力比度中心性、介数中心性、K-shell分解、混合度分解等方法更准确。展开更多
基金supported by the National Natural Science Foundation of China(61174022)the National High Technology Research and Development Program of China(863 Program)(2013AA013801)+2 种基金the Open Funding Project of State Key Laboratory of Virtual Reality Technology and Systems,Beihang University(BUAA-VR-14KF-02)the General Research Program of the Science Supported by Sichuan Provincial Department of Education(14ZB0322)the Fundamental Research Funds for the Central Universities(XDJK2014D008)
文摘Identifying influential nodes in complex networks is still an open issue. In this paper, a new comprehensive centrality mea- sure is proposed based on the Dempster-Shafer evidence theory. The existing measures of degree centrality, betweenness centra- lity and closeness centrality are taken into consideration in the proposed method. Numerical examples are used to illustrate the effectiveness of the proposed method.
文摘In order to assess influential nodes in complex networks, the authors propose a novel ranking method based on structural hole in combination with the degree ratio of a node and its neighbors. The proposed method is a response to the limitations of other proposed measures in this field. The structural hole gives a comprehensive attention of the information about the node topology in relation to its neighbors, whereas the degree ratio of nodes reflects its significance against the neighbors.Combination of the two aforementioned measures summarized in the structural hole leverage matrix demonstrates the importance of a node according to its position in the network structure. So a more accurate method for ranking influential nodes is established. The simulation results over different-scale networks(small networks with less than 30 nodes, medium networks with less than 150 nodes and large networks with more than 1000 nodes) suggest that the proposed method can rank important nodes more effectively and precisely in complex networks specifically in larger ones.
基金Supported by the Research Foundation of Hubei Province Department of Education(Q20151505)the East China Jiaotong University Doctor Scientific Research Start Fund Project(26441021)
文摘The identification of the influential nodes in a network is of great significance for understanding the features of the network and controlling the complexity of networks in society and in biology. In this paper, we propose a novel centrality measure for a node by considering the importance of edges and compare the performance of this method with existing seven topological-based ranking methods on the Susceptible-Infected-Recovered （SIR） model. The simulation results for four different types of real networks show that the proposed method is robust and exhibits excellent performance in identifying the most influential nodes when spreading starting from both single origin and multipleorigins simultaneously.
文摘准确高效地发现网络中有影响力的传播者具有非常重要的理论和现实意义。近年来,结点影响力排序受到了多领域学者的广泛关注。K-shell是一种较好的结点影响力评价指标;然而,仅仅依赖结点自身K-shell值实现的算法通常具有评估结果精确度不高、适用性较差等缺陷。针对此问题,提出KSN(the K-shell and neighborhood centrality)中心性模型,该算法综合考虑了结点本身及其所有二阶以内邻居结点的K-shell值。实验结果表明,所提出算法度量结点传播的能力比度中心性、介数中心性、K-shell分解、混合度分解等方法更准确。
