Identifying the community structure of complex networks is crucial to extracting insights and understanding network properties.Although several community detection methods have been proposed,many are unsuitable for so...Identifying the community structure of complex networks is crucial to extracting insights and understanding network properties.Although several community detection methods have been proposed,many are unsuitable for social networks due to significant limitations.Specifically,most approaches depend mainly on user-user structural links while overlooking service-centric,semantic,and multi-attribute drivers of community formation,and they also lack flexible filtering mechanisms for large-scale,service-oriented settings.Our proposed approach,called community discovery-based service(CDBS),leverages user profiles and their interactions with consulted web services.The method introduces a novel similarity measure,global similarity interaction profile(GSIP),which goes beyond typical similarity measures by unifying user and service profiles for all attributes types into a coherent representation,thereby clarifying its novelty and contribution.It applies multiple filtering criteria related to user attributes,accessed services,and interaction patterns.Experimental comparisons against Louvain,Hierarchical Agglomerative Clustering,Label Propagation and Infomap show that CDBS reveals the higher performance as it achieves 0.74 modularity,0.13 conductance,0.77 coverage,and significantly fast response time of 9.8 s,even with 10,000 users and 400 services.Moreover,community discoverybased service consistently detects a larger number of communities with distinct topics of interest,underscoring its capacity to generate detailed and efficient structures in complex networks.These results confirm both the efficiency and effectiveness of the proposed method.Beyond controlled evaluation,communities discovery based service is applicable to targeted recommendations,group-oriented marketing,access control,and service personalization,where communities are shaped not only by user links but also by service engagement.展开更多
The traffic congestion occurs frequently in urban areas, while most existing solutions only take effects after congesting. In this paper, a congestion warning method is proposed based on the Internet of vehicles（IOV...The traffic congestion occurs frequently in urban areas, while most existing solutions only take effects after congesting. In this paper, a congestion warning method is proposed based on the Internet of vehicles（IOV） and community discovery of complex networks. The communities in complex network model of traffic flow reflect the local aggregation of vehicles in the traffic system, and it is used to predict the upcoming congestion. The real-time information of vehicles on the roads is obtained from the IOV, which includes the locations, speeds and orientations of vehicles. Then the vehicles are mapped into nodes of network, the links between nodes are determined by the correlations between vehicles in terms of location and speed. The complex network model of traffic flow is hereby established. The communities in this complex network are discovered by fast Newman（FN） algorithm, and the congestion warnings are generated according to the communities selected by scale and density. This method can detect the tendency of traffic aggregation and provide warnings before congestion occurs. The simulations show that the method proposed in this paper is effective and practicable, and makes it possible to take action before traffic congestion.展开更多
文摘Identifying the community structure of complex networks is crucial to extracting insights and understanding network properties.Although several community detection methods have been proposed,many are unsuitable for social networks due to significant limitations.Specifically,most approaches depend mainly on user-user structural links while overlooking service-centric,semantic,and multi-attribute drivers of community formation,and they also lack flexible filtering mechanisms for large-scale,service-oriented settings.Our proposed approach,called community discovery-based service(CDBS),leverages user profiles and their interactions with consulted web services.The method introduces a novel similarity measure,global similarity interaction profile(GSIP),which goes beyond typical similarity measures by unifying user and service profiles for all attributes types into a coherent representation,thereby clarifying its novelty and contribution.It applies multiple filtering criteria related to user attributes,accessed services,and interaction patterns.Experimental comparisons against Louvain,Hierarchical Agglomerative Clustering,Label Propagation and Infomap show that CDBS reveals the higher performance as it achieves 0.74 modularity,0.13 conductance,0.77 coverage,and significantly fast response time of 9.8 s,even with 10,000 users and 400 services.Moreover,community discoverybased service consistently detects a larger number of communities with distinct topics of interest,underscoring its capacity to generate detailed and efficient structures in complex networks.These results confirm both the efficiency and effectiveness of the proposed method.Beyond controlled evaluation,communities discovery based service is applicable to targeted recommendations,group-oriented marketing,access control,and service personalization,where communities are shaped not only by user links but also by service engagement.
基金supported by the National Natural Science Foundation of China(61433003,61273150)the Beijing Higher Education Young Elite Teacher Project(YETP1192)
文摘The traffic congestion occurs frequently in urban areas, while most existing solutions only take effects after congesting. In this paper, a congestion warning method is proposed based on the Internet of vehicles（IOV） and community discovery of complex networks. The communities in complex network model of traffic flow reflect the local aggregation of vehicles in the traffic system, and it is used to predict the upcoming congestion. The real-time information of vehicles on the roads is obtained from the IOV, which includes the locations, speeds and orientations of vehicles. Then the vehicles are mapped into nodes of network, the links between nodes are determined by the correlations between vehicles in terms of location and speed. The complex network model of traffic flow is hereby established. The communities in this complex network are discovered by fast Newman（FN） algorithm, and the congestion warnings are generated according to the communities selected by scale and density. This method can detect the tendency of traffic aggregation and provide warnings before congestion occurs. The simulations show that the method proposed in this paper is effective and practicable, and makes it possible to take action before traffic congestion.
