Cellular networks are overloaded due to the mobile traffic surge,and mobile social networks(MSNets) can be leveraged for traffic offloading.In this paper,we study the issue of choosing seed users for maximizing the mo...Cellular networks are overloaded due to the mobile traffic surge,and mobile social networks(MSNets) can be leveraged for traffic offloading.In this paper,we study the issue of choosing seed users for maximizing the mobile traffic offloaded from cellular networks.We introduce a gossip-style social cascade(GSC) model to model the epidemic-like information diffusion process in MSNets.For static-case and mobile-case networks,we establish an equivalent view and a temporal mapping of the information diffusion process,respectively.We further prove the submodularity in the information diffusion and propose a greedy algorithm to choose the seed users for traffic offloading,yielding a sub-optimal solution to the NP-hard traffic offloading maximization(TOM) problem.Experiments are carried out to study the offloading performance,illustrating that the greedy algorithm significantly outperforms the heuristic and random algorithms,and user mobility can help further reduce cellular load.展开更多
With the development of Web 2.0 and the gradual maturity of online social media, social network has become an indispensable platform for people＇ s social interaction, information sharing and news transmission.The stu...With the development of Web 2.0 and the gradual maturity of online social media, social network has become an indispensable platform for people＇ s social interaction, information sharing and news transmission.The study of social networks has become the consensus and urgent need of the academic and industrial circles. The prediction of information communication is the core function of social network, and it is also the core content.To be specific, this paper briefly introduces the concept of social network, then summarizes the diffusion prediction models and methods from five aspects, namely, user attributes-based, information characteristics-based,user groups-based, statistic and inference-based and network topology -based models. Finally, the directions for the future research are discussed.展开更多
基金supported by the National Basic Research Program of China(973 Program) through grant 2012CB316004the Doctoral Program of Higher Education(SRFDP)+1 种基金Research Grants Council Earmarked Research Grants(RGC ERG) Joint Research Scheme through Specialized Research Fund 20133402140001National Natural Science Foundation of China through grant 61379003
文摘Cellular networks are overloaded due to the mobile traffic surge,and mobile social networks(MSNets) can be leveraged for traffic offloading.In this paper,we study the issue of choosing seed users for maximizing the mobile traffic offloaded from cellular networks.We introduce a gossip-style social cascade(GSC) model to model the epidemic-like information diffusion process in MSNets.For static-case and mobile-case networks,we establish an equivalent view and a temporal mapping of the information diffusion process,respectively.We further prove the submodularity in the information diffusion and propose a greedy algorithm to choose the seed users for traffic offloading,yielding a sub-optimal solution to the NP-hard traffic offloading maximization(TOM) problem.Experiments are carried out to study the offloading performance,illustrating that the greedy algorithm significantly outperforms the heuristic and random algorithms,and user mobility can help further reduce cellular load.
文摘With the development of Web 2.0 and the gradual maturity of online social media, social network has become an indispensable platform for people＇ s social interaction, information sharing and news transmission.The study of social networks has become the consensus and urgent need of the academic and industrial circles. The prediction of information communication is the core function of social network, and it is also the core content.To be specific, this paper briefly introduces the concept of social network, then summarizes the diffusion prediction models and methods from five aspects, namely, user attributes-based, information characteristics-based,user groups-based, statistic and inference-based and network topology -based models. Finally, the directions for the future research are discussed.
