当前,民航旅客价值分析把每一个旅客当作彼此不相关联的实体,忽略了旅客间存在的关系。针对这种情况,提出从旅客间的相互影响角度出发,量化这种影响的强弱。基于PNR(Passenger Name Record)数据构建民航旅客社会网络,从系统科学、网络...当前,民航旅客价值分析把每一个旅客当作彼此不相关联的实体,忽略了旅客间存在的关系。针对这种情况,提出从旅客间的相互影响角度出发,量化这种影响的强弱。基于PNR(Passenger Name Record)数据构建民航旅客社会网络,从系统科学、网络关系和互联网搜索这三个角度研究社会网络中节点重要性的评估算法,并把这三种算法应用在民航旅客社会网络中。最后,通过F-度量方法对这三种算法计算出的重要节点进行相似性比较。实验结果表明,该方法能够有效地得到民航旅客社会网络中的重要旅客。展开更多
Community structure is an important characteristic in real complex network.It is a network consists ofgroups of nodes within which links are dense but among which links are sparse.In this paper, the evolving network i...Community structure is an important characteristic in real complex network.It is a network consists ofgroups of nodes within which links are dense but among which links are sparse.In this paper, the evolving network includenode, link and community growth and we apply the community size preferential attachment and strength preferentialattachment to a growing weighted network model and utilize weight assigning mechanism from BBV model.Theresulting network reflects the intrinsic community structure with generalized power-law distributions of nodes'degreesand strengths.展开更多
In a social network analysis the output provided includes many measures and metrics. For each of these measures and metric, the output provides the ability to obtain a rank ordering of the nodes in terms of these meas...In a social network analysis the output provided includes many measures and metrics. For each of these measures and metric, the output provides the ability to obtain a rank ordering of the nodes in terms of these measures. We might use this information in decision making concerning disrupting or deceiving a given network. All is fine when all the measures indicate the same node as the key or influential node. What happens when the measures indicate different key nodes？ Our goal in this paper is to explore two methodologies to identify the key players or nodes in a given network. We apply TOPSIS to analyze these outputs to find the most influential nodes as a function of the decision makers＇ inputs as a process to consider both subjective and objectives inputs through pairwise comparison matrices. We illustrate our results using two common networks from the literature： the Kite network and the Information flow network from Knoke and Wood. We discuss some basic sensitivity analysis can may be applied to the methods. We find the use of TOPSIS as a flexible method to weight the criterion based upon the decision makers＇ inputs or the topology of the network.展开更多
Nodes play different roles or have different functions in many natural and social networks.In this paper,a simple model with different types of nodes and deterministic selective linking rule is proposed.The structural...Nodes play different roles or have different functions in many natural and social networks.In this paper,a simple model with different types of nodes and deterministic selective linking rule is proposed.The structural properties by theoretical predictions are investigated that the given model exhibits a power-law distribution.展开更多
文摘当前,民航旅客价值分析把每一个旅客当作彼此不相关联的实体,忽略了旅客间存在的关系。针对这种情况,提出从旅客间的相互影响角度出发,量化这种影响的强弱。基于PNR(Passenger Name Record)数据构建民航旅客社会网络,从系统科学、网络关系和互联网搜索这三个角度研究社会网络中节点重要性的评估算法,并把这三种算法应用在民航旅客社会网络中。最后,通过F-度量方法对这三种算法计算出的重要节点进行相似性比较。实验结果表明,该方法能够有效地得到民航旅客社会网络中的重要旅客。
基金Supported by the National Nature Science Foundation of China under Grant No.10832006PuJiang Project of Shanghai under Grant No.09PJ1405000+1 种基金Key Disciplines of Shanghai Municipality (S30104)Research Grant of Shanghai University under Grant No.SHUCX092014
文摘Community structure is an important characteristic in real complex network.It is a network consists ofgroups of nodes within which links are dense but among which links are sparse.In this paper, the evolving network includenode, link and community growth and we apply the community size preferential attachment and strength preferentialattachment to a growing weighted network model and utilize weight assigning mechanism from BBV model.Theresulting network reflects the intrinsic community structure with generalized power-law distributions of nodes'degreesand strengths.
文摘In a social network analysis the output provided includes many measures and metrics. For each of these measures and metric, the output provides the ability to obtain a rank ordering of the nodes in terms of these measures. We might use this information in decision making concerning disrupting or deceiving a given network. All is fine when all the measures indicate the same node as the key or influential node. What happens when the measures indicate different key nodes？ Our goal in this paper is to explore two methodologies to identify the key players or nodes in a given network. We apply TOPSIS to analyze these outputs to find the most influential nodes as a function of the decision makers＇ inputs as a process to consider both subjective and objectives inputs through pairwise comparison matrices. We illustrate our results using two common networks from the literature： the Kite network and the Information flow network from Knoke and Wood. We discuss some basic sensitivity analysis can may be applied to the methods. We find the use of TOPSIS as a flexible method to weight the criterion based upon the decision makers＇ inputs or the topology of the network.
文摘Nodes play different roles or have different functions in many natural and social networks.In this paper,a simple model with different types of nodes and deterministic selective linking rule is proposed.The structural properties by theoretical predictions are investigated that the given model exhibits a power-law distribution.
