P2P流媒体网络的关键节点识别算法
|
摘要
P2P流媒体网络中普遍存在一些关键节点,关键节点对网络的安全和通信性能起着重要作用,识别网络中的关键节点尤为重要,而传统方法对于大规模网络的关键节点识别时间开销很大,无法保证实时性。提出P2P流媒体网络中的关键节点识别算法,结合混合模式的网络结构特点,采用分区域的计算模型解决网络规模过大造成的巨大时间开销问题,根据节点的贡献度和传播能力差异定量化描述节点的重要性程度。仿真结果表明,所提算法可以快速获得节点重要性排序,有效识别P2P流媒体网络中的关键节点。
There are some key nodes in P2 Pstreaming networks,which play an important role in network security and network communication.So identifying key nodes in the network is very crucial.The traditional method has huge time overhead for key node identification in large-scale networks and cannot guarantee real-time performance.We propose a key node identification algorithm in P2 Pstreaming media networks.Combining with the network structure characteristics of the hybrid mode,we use a region-based computing model to solve the huge time-consumption problem caused by the excessive network scale.The importance of nodes is quantitatively described according to the contribution and propagation capacity of nodes.Simulation results show that the proposed algorithm can quickly obtain the results of node importance ranking and effectively identify the key nodes in P2 Pstreaming networks.
There are some key nodes in P2 Pstreaming networks,which play an important role in network security and network communication.So identifying key nodes in the network is very crucial.The traditional method has huge time overhead for key node identification in large-scale networks and cannot guarantee real-time performance.We propose a key node identification algorithm in P2 Pstreaming media networks.Combining with the network structure characteristics of the hybrid mode,we use a region-based computing model to solve the huge time-consumption problem caused by the excessive network scale.The importance of nodes is quantitatively described according to the contribution and propagation capacity of nodes.Simulation results show that the proposed algorithm can quickly obtain the results of node importance ranking and effectively identify the key nodes in P2 Pstreaming networks.
引文
[1]Timpl R,Wiedemann H,Delden V,et al.A network model for the organization of type IV collagen molecules in basement membranes[J].European Journal of Biochemistry,1981,120(2):203-211.
[2]Bridle J S.Training stochastic model recognition algorithms as networks can lead to maximum mutual information estimation of parameters[J].Advances in Neural Information Processing Systems,1989,17(2):211-217.
[3]Newman M E J,Watts D J.Renormalization group analysis of the small-world network model[J].Physics Letters A,1999,263(4):341-346.
[4]Barabási A L,Albert R,Jeong H.Mean-field theory for scalefree random networks[J].Physica A:Statistical Mechanics and Its Applications,1999,272(1):173-187.
[5]Achard S,Salvador R,Whitcher B,et al.A resilient,low-frequency,small-world human brain functional network with highly connected association cortical hubs[J].Journal of Neuroscience,2006,26(1):63-72.
[6]Fu F,Liu L H,Wang L.Evolutionary prisoner’s dilemma on heterogeneous Newman-Watts small-world network[J].The European Physical Journal B-Condensed Matter and Complex Systems,2007,56(4):1-10.
[7]Bader D A,Madduri K.Snap,small-world network analysis and partitioning:An open-source parallel graph framework for the exploration of large-scale networks[C]∥Proc of IEEE International Symposium on Parallel and Distributed Processing,2008:1-12.
[8]Huang J,Yin B,Guo D,et al.An evolution model for P2Pfile-sharing networks[C]∥Proc of the 2nd International Conference on Computer Modeling and Simulation,2010:361-365.
[9]Gnutella[EB/OL].[2013-03-01].http://gnutella.wego.com/.
[10]Ripeanu M,Iamnitchi A,Foster I.Mapping the Gnutella network[J].IEEE Internet Computing,2002,6(1):50-57.
[11]Wang Xiao-lei,Yang Yue-xiang,He Jie.Traffic statisticsbased identification of key nodes in P2Pnetwork[J].Application Research of Computers,2015,32(5):1445-1449.(in Chinese)
[11]王晓磊,杨岳湘,何杰.基于流量统计的P2P网络关键节点识别[J].计算机应用研究,2015,32(5):1445-1449.
[2]Bridle J S.Training stochastic model recognition algorithms as networks can lead to maximum mutual information estimation of parameters[J].Advances in Neural Information Processing Systems,1989,17(2):211-217.
[3]Newman M E J,Watts D J.Renormalization group analysis of the small-world network model[J].Physics Letters A,1999,263(4):341-346.
[4]Barabási A L,Albert R,Jeong H.Mean-field theory for scalefree random networks[J].Physica A:Statistical Mechanics and Its Applications,1999,272(1):173-187.
[5]Achard S,Salvador R,Whitcher B,et al.A resilient,low-frequency,small-world human brain functional network with highly connected association cortical hubs[J].Journal of Neuroscience,2006,26(1):63-72.
[6]Fu F,Liu L H,Wang L.Evolutionary prisoner’s dilemma on heterogeneous Newman-Watts small-world network[J].The European Physical Journal B-Condensed Matter and Complex Systems,2007,56(4):1-10.
[7]Bader D A,Madduri K.Snap,small-world network analysis and partitioning:An open-source parallel graph framework for the exploration of large-scale networks[C]∥Proc of IEEE International Symposium on Parallel and Distributed Processing,2008:1-12.
[8]Huang J,Yin B,Guo D,et al.An evolution model for P2Pfile-sharing networks[C]∥Proc of the 2nd International Conference on Computer Modeling and Simulation,2010:361-365.
[9]Gnutella[EB/OL].[2013-03-01].http://gnutella.wego.com/.
[10]Ripeanu M,Iamnitchi A,Foster I.Mapping the Gnutella network[J].IEEE Internet Computing,2002,6(1):50-57.
[11]Wang Xiao-lei,Yang Yue-xiang,He Jie.Traffic statisticsbased identification of key nodes in P2Pnetwork[J].Application Research of Computers,2015,32(5):1445-1449.(in Chinese)
[11]王晓磊,杨岳湘,何杰.基于流量统计的P2P网络关键节点识别[J].计算机应用研究,2015,32(5):1445-1449.