以Web of science数据库为数据源,采用加权网络分析方法,构建了国际科学合作网络。从网络分析的角度,研究各国在合作网络中的重要性,揭示了合作网络中的社团结构。研究结果表明:合作网络的密度在逐年增加,各国的合作范围、合作强度逐年...以Web of science数据库为数据源,采用加权网络分析方法,构建了国际科学合作网络。从网络分析的角度,研究各国在合作网络中的重要性,揭示了合作网络中的社团结构。研究结果表明:合作网络的密度在逐年增加,各国的合作范围、合作强度逐年增强,跨国合作呈现多极化发展的趋势,但整体格局未发生根本性变化;社团结构的形成受地域、文化、语言等因素的影响,表现出和经济区域化的相似性。展开更多
Being a huge system, Internet topology structure is very complex. It can’t be treated as a plane simply, and its hierarchy must be analyzed. We used the k-core decomposition to disentangle the hierarchical structure ...Being a huge system, Internet topology structure is very complex. It can’t be treated as a plane simply, and its hierarchy must be analyzed. We used the k-core decomposition to disentangle the hierarchical structure of Internet Router-level topology. By analyzing the router-lever Internet topology measuring data from CAIDA (The Cooperative Association for Internet Data Analysis) ,we studied the characteristics of the nodes in the inner hierarchy and outer hierarchy respectively. The frequency-degree power law of the nodes which core-ness is lower and the regionally distribution of the nodes which coreness is higher were concluded. At last, the topology of every hierarchy was described by giving their figures. These descriptions can provide a valuable reference for modeling on the Internet topology.展开更多
The influence maximization is the problem of finding k seed nodes that maximize the scope of influence in a social network.Therefore,the comprehensive influence of node needs to be considered,when we choose the most i...The influence maximization is the problem of finding k seed nodes that maximize the scope of influence in a social network.Therefore,the comprehensive influence of node needs to be considered,when we choose the most influential node set consisted of k seed nodes.On account of the traditional methods used to measure the influence of nodes,such as degree centrality,betweenness centrality and closeness centrality,consider only a single aspect of the influence of node,so the influence measured by traditional methods mentioned above of node is not accurate.In this paper,we obtain the following result through experimental analysis:the influence of a node is relevant not only to its degree and coreness,but also to the degree and coreness of the n-order neighbor nodes.Hence,we propose a algorithm based on the mixed importance of nodes to measure the comprehensive influence of node,and the algorithm we proposed is simple and efficient.In addition,the performance of the algorithm we proposed is better than that of traditional influence maximization algorithms.展开更多
文摘以Web of science数据库为数据源,采用加权网络分析方法,构建了国际科学合作网络。从网络分析的角度,研究各国在合作网络中的重要性,揭示了合作网络中的社团结构。研究结果表明:合作网络的密度在逐年增加,各国的合作范围、合作强度逐年增强,跨国合作呈现多极化发展的趋势,但整体格局未发生根本性变化;社团结构的形成受地域、文化、语言等因素的影响,表现出和经济区域化的相似性。
文摘Being a huge system, Internet topology structure is very complex. It can’t be treated as a plane simply, and its hierarchy must be analyzed. We used the k-core decomposition to disentangle the hierarchical structure of Internet Router-level topology. By analyzing the router-lever Internet topology measuring data from CAIDA (The Cooperative Association for Internet Data Analysis) ,we studied the characteristics of the nodes in the inner hierarchy and outer hierarchy respectively. The frequency-degree power law of the nodes which core-ness is lower and the regionally distribution of the nodes which coreness is higher were concluded. At last, the topology of every hierarchy was described by giving their figures. These descriptions can provide a valuable reference for modeling on the Internet topology.
基金This research was supported in part by the Chinese National Natural Science Foundation under grant Nos.61602202 and 61702441the Natural Science Foundation of Jiangsu Province under contracts BK20160428 and BK20161302the Six talent peaks project in Jiangsu Province under contract XYDXX-034 and the project in Jiangsu Association for science and technology.
文摘The influence maximization is the problem of finding k seed nodes that maximize the scope of influence in a social network.Therefore,the comprehensive influence of node needs to be considered,when we choose the most influential node set consisted of k seed nodes.On account of the traditional methods used to measure the influence of nodes,such as degree centrality,betweenness centrality and closeness centrality,consider only a single aspect of the influence of node,so the influence measured by traditional methods mentioned above of node is not accurate.In this paper,we obtain the following result through experimental analysis:the influence of a node is relevant not only to its degree and coreness,but also to the degree and coreness of the n-order neighbor nodes.Hence,we propose a algorithm based on the mixed importance of nodes to measure the comprehensive influence of node,and the algorithm we proposed is simple and efficient.In addition,the performance of the algorithm we proposed is better than that of traditional influence maximization algorithms.
