社会网络特征分析与社团结构挖掘
|
摘要
社会网络是用来描述社会成员之间关系的网络。随着计算机网络技术的迅猛发展，基于互联网的电子邮件网络、对等网络、社交网络和博客网络等新兴社会网络也得到了极大的发展。对社会网络开展研究，具有重要的理论意义和实用价值。由于社会网络往往规模庞大、结构复杂，所以我们通常采用复杂网络的理论和模型来对社会网络进行研究和建模。
真实社会网络通常具有复杂网络的一些结构特征，如:社团结构，无标度的度分布，聚类、“小世界”网络、动态演变等。社团结构是社会网络的一个重要结构特征，准确检测复杂社会网络的社团结构是近年来社会网络研究的重点。在社团结构的检测中，模块度起了很重要的作用，它可以用来评价社团结构划分的质量。另外由于社会网络消耗了互联网上的大部份流量，对社会网络进行测量也是一项重要工作，有助于我们深入了解社会网络的拓扑特征，监控网络流量，保障网络安全。本论文的主要研究工作是基于复杂网络理论挖掘社会网络的社团结构，统计分析其结构特征，并对社会网络进行主动测量，从而揭示真实社会网络的演化规律。本论文取得的创新性成果如下：
(1)提出了一个基于有向加权模块度的静态社会网络社团结构检测方法
基于无向和有向网络、无权和加权网络社团结构分析的可统一性，提出了一个新的模块度最优化的静态社会网络社团结构检测方法SNCD。该方法引入了边的方向和权重，使用有向加权模块度来进行社团结构的检测。由于社团划分时可参考的有用信息更多，从而提高了算法的准确性和有效性。该方法使用堆结构和多任务的模块架构，大幅度提高了计算效率，在结点数目达到百万级的网络规模下，仍能获得较优的执行效率，并且社团划分的质量高于当前应用较广泛的一些典型算法。
(2)设计了一个基于时间序列的动态社会网络社团结构检测模型
将社会网络的时序动态性和时刻静态性用时间序列的方式来表示，即社会网络在时间序列的每一个时刻是静态的，同时又随时间序列动态演变。通过使用基于结构相似度的模块度新定义，设计了一个基于时间序列的动态社会网络社团结构检测模型。该模型首先提出了一个静态社会网络社团结构检测方法LMA，用以检测时间序列上每一个时刻的静态社团集合，为下一步DNCD方法提供中间过程社团集合。随后扩展LMA方法，提出了一个基于时间序列的动态社会网络社团结构检测方法DNCD。该方法将中间过程社团集合与社团时间序列链进行匹配，以得到社团演变的轨迹和反映网络结构的稳定社团集合。
(3)提出了一个社会网络主动测量策略
为了深入了解社会网络的结构特征，监控网络的实时状态，提出了一个多协议模块化的社会网络主动测量策略。以对等网络中的BitTorrent协议作为测量对象，采用拟定的主动测量策略，设计了相应的测量指标，实现了对真实社会网络的实时测量，揭示了真实社会网络的内在结构特征和行为模式。
The relations among the members of society are usually described by socialnetworks. With the rapid development of computer network technology, the emergingsocial networks based on Internet such as E-mail networks, peer-to-peer networks,social networks and blog networks develope greatly, and give a profound influence onthe behavior patterns of the human society. Therefore, research on the social networks ismeaningful both in theory and in practice. The social networks are often large-scale andcomplex in structure, therefore complex networks theories and models are adopted tostudy the social networks.
Real social networks usually have some structural features of the complexnetworks, such as community structure, the scale-free degree distribution, clustering,"small world" network, dynamic evolution and so on. The community structure is animportant structural feature of social networks. Community detecting of the complexsocial networks is the focus of the social networks study in recent years. Modularity hasplayed a very important role in community detecting. It has been used to evaluate thequality of community structure detecting. In addition, the social networks consume alarge portion of traffic on the Internet; therefore the social networks measurement is alsovery important. It can provide an insight into the topology characteristics of the socialnetworks, monitor network traffic, and ensure network security. The main research workof the thesis is mining the community structure of the social networks based on complexnetwork theories, analyzing the structural characteristics, and monitoring the socialnetworks actively. Through those works, the evolution of the real social networks isrevealed. The innovative achievements of this thesis are as follows:
(1) A community detecting method of the static social networks based on thedirected and weighted modularity is proposed.
Based on the unity of the undirected/directed networks and theunweighted/weighted networks, a new community detecting method SNCD of the staticsocial networks is proposed using the new modularity optimization method. Byintroducing the edge direction and weight, the directed and weighted modularity is used to detect the community structure. The accuracy and effectiveness of communitydetecting dramatically improved with much more available information utilized. Themethod uses the heap structure and multi-tasking modular architecture, which increasesthe computational efficiency greatly. The method can worked efficiently on large scalenetworks which have millions of nodes. At the same time, the dividing accuracy of themethod improves largely comparing to the typical methods widely used currently.
(2) A community detecting model of the dynamic social networks based on timeseries is designed.
Time series is used to describe the dynamic and the static characters of the socialnetworks. That is, the social networks are cross-sectionally static but evolutedynamically on whole time series. By introducing a new modularity definition based onstructural similarity, a new community detecting model of the dynamic social networksbased on time series is designed. The model first proposes a static community detectingmethod LMA to detect the static community set for every moment of the time series,and the same time provides the intermediate community set for next DNCD method.Then by extending the LMA method, DNCD method is proposed to detect dynamiccommunity of the dynamic social networks based on time series. After that, the DNCDmethod combines the intermediate community set with the community time-sequencechain, and obtains the evolution trajectory of community structure and the final stablenetwork structure.
(3) A social network active measurement strategy is proposed.
To better understanding the structural features of the social networks andmonitoring the real-time status of the networks, a multi-protocol and modular activemeasurement strategy for the social networks is proposed. Using the BitTorrent protocolof peer-to-peer network as the measurement subject, the active measurement strategy isadopted, and the measurement indexes are designed. The real-time measurement of thereal social networks is realized, and the inherent structural characteristics and behavioralpatterns of the real social networks are revealed. Taking the BitTorrent protocol ofpeer-to-peer network as measuring subject, and by designing corresponding measuringindexes and adopting active measuring strategies, we realize real-time measuring of thereal social network and reveal the inherent structure and behavior patterns of the realsocial network.
真实社会网络通常具有复杂网络的一些结构特征，如:社团结构，无标度的度分布，聚类、“小世界”网络、动态演变等。社团结构是社会网络的一个重要结构特征，准确检测复杂社会网络的社团结构是近年来社会网络研究的重点。在社团结构的检测中，模块度起了很重要的作用，它可以用来评价社团结构划分的质量。另外由于社会网络消耗了互联网上的大部份流量，对社会网络进行测量也是一项重要工作，有助于我们深入了解社会网络的拓扑特征，监控网络流量，保障网络安全。本论文的主要研究工作是基于复杂网络理论挖掘社会网络的社团结构，统计分析其结构特征，并对社会网络进行主动测量，从而揭示真实社会网络的演化规律。本论文取得的创新性成果如下：
(1)提出了一个基于有向加权模块度的静态社会网络社团结构检测方法
基于无向和有向网络、无权和加权网络社团结构分析的可统一性，提出了一个新的模块度最优化的静态社会网络社团结构检测方法SNCD。该方法引入了边的方向和权重，使用有向加权模块度来进行社团结构的检测。由于社团划分时可参考的有用信息更多，从而提高了算法的准确性和有效性。该方法使用堆结构和多任务的模块架构，大幅度提高了计算效率，在结点数目达到百万级的网络规模下，仍能获得较优的执行效率，并且社团划分的质量高于当前应用较广泛的一些典型算法。
(2)设计了一个基于时间序列的动态社会网络社团结构检测模型
将社会网络的时序动态性和时刻静态性用时间序列的方式来表示，即社会网络在时间序列的每一个时刻是静态的，同时又随时间序列动态演变。通过使用基于结构相似度的模块度新定义，设计了一个基于时间序列的动态社会网络社团结构检测模型。该模型首先提出了一个静态社会网络社团结构检测方法LMA，用以检测时间序列上每一个时刻的静态社团集合，为下一步DNCD方法提供中间过程社团集合。随后扩展LMA方法，提出了一个基于时间序列的动态社会网络社团结构检测方法DNCD。该方法将中间过程社团集合与社团时间序列链进行匹配，以得到社团演变的轨迹和反映网络结构的稳定社团集合。
(3)提出了一个社会网络主动测量策略
为了深入了解社会网络的结构特征，监控网络的实时状态，提出了一个多协议模块化的社会网络主动测量策略。以对等网络中的BitTorrent协议作为测量对象，采用拟定的主动测量策略，设计了相应的测量指标，实现了对真实社会网络的实时测量，揭示了真实社会网络的内在结构特征和行为模式。
The relations among the members of society are usually described by socialnetworks. With the rapid development of computer network technology, the emergingsocial networks based on Internet such as E-mail networks, peer-to-peer networks,social networks and blog networks develope greatly, and give a profound influence onthe behavior patterns of the human society. Therefore, research on the social networks ismeaningful both in theory and in practice. The social networks are often large-scale andcomplex in structure, therefore complex networks theories and models are adopted tostudy the social networks.
Real social networks usually have some structural features of the complexnetworks, such as community structure, the scale-free degree distribution, clustering,"small world" network, dynamic evolution and so on. The community structure is animportant structural feature of social networks. Community detecting of the complexsocial networks is the focus of the social networks study in recent years. Modularity hasplayed a very important role in community detecting. It has been used to evaluate thequality of community structure detecting. In addition, the social networks consume alarge portion of traffic on the Internet; therefore the social networks measurement is alsovery important. It can provide an insight into the topology characteristics of the socialnetworks, monitor network traffic, and ensure network security. The main research workof the thesis is mining the community structure of the social networks based on complexnetwork theories, analyzing the structural characteristics, and monitoring the socialnetworks actively. Through those works, the evolution of the real social networks isrevealed. The innovative achievements of this thesis are as follows:
(1) A community detecting method of the static social networks based on thedirected and weighted modularity is proposed.
Based on the unity of the undirected/directed networks and theunweighted/weighted networks, a new community detecting method SNCD of the staticsocial networks is proposed using the new modularity optimization method. Byintroducing the edge direction and weight, the directed and weighted modularity is used to detect the community structure. The accuracy and effectiveness of communitydetecting dramatically improved with much more available information utilized. Themethod uses the heap structure and multi-tasking modular architecture, which increasesthe computational efficiency greatly. The method can worked efficiently on large scalenetworks which have millions of nodes. At the same time, the dividing accuracy of themethod improves largely comparing to the typical methods widely used currently.
(2) A community detecting model of the dynamic social networks based on timeseries is designed.
Time series is used to describe the dynamic and the static characters of the socialnetworks. That is, the social networks are cross-sectionally static but evolutedynamically on whole time series. By introducing a new modularity definition based onstructural similarity, a new community detecting model of the dynamic social networksbased on time series is designed. The model first proposes a static community detectingmethod LMA to detect the static community set for every moment of the time series,and the same time provides the intermediate community set for next DNCD method.Then by extending the LMA method, DNCD method is proposed to detect dynamiccommunity of the dynamic social networks based on time series. After that, the DNCDmethod combines the intermediate community set with the community time-sequencechain, and obtains the evolution trajectory of community structure and the final stablenetwork structure.
(3) A social network active measurement strategy is proposed.
To better understanding the structural features of the social networks andmonitoring the real-time status of the networks, a multi-protocol and modular activemeasurement strategy for the social networks is proposed. Using the BitTorrent protocolof peer-to-peer network as the measurement subject, the active measurement strategy isadopted, and the measurement indexes are designed. The real-time measurement of thereal social networks is realized, and the inherent structural characteristics and behavioralpatterns of the real social networks are revealed. Taking the BitTorrent protocol ofpeer-to-peer network as measuring subject, and by designing corresponding measuringindexes and adopting active measuring strategies, we realize real-time measuring of thereal social network and reveal the inherent structure and behavior patterns of the realsocial network.
引文
[1] D. Watts, S. Strogatz. The small world problem[J]. Collective Dynamics of Small-WorldNetworks,1998,393(2):440-442.
[2] A. L. Barabási, R. Albert. Emergence of scaling in random networks[J]. Science,1999,286(5439):509-512.
[3] S. H. Strogatz. Exploring complex networks[J]. Nature,2001,410(6825):268-276.
[4]杨树忠.复杂网络中的社团检测问题研究[D].北京:北京交通大学,2009,3-4.
[5] D. Simard, L. Nadeau, H. Kr ger. Fastest learning in small-world neural networks[J].Physics Letters A,2005,336(1):8-15.
[6] J. J. Torres, M. A. Munoz, J. Marro, et al. Influence of topology on the performance of aneural network[J]. Neurocomputing,2004,58(6):229-234.
[7] S. Yang, S. Luo, J. Li. Building multi-layer small world neural network[J]. Advances inNeural Networks,2006,10(1):695-700.
[8] S. Yang, S. Luo, J. Li. An extended model on self-organizing map[J]. Neural InformationProcessing,2006,6(2):987-994.
[9] D. Stauffer, A. Aharony, L. da Fontoura Costa, et al. Efficient hopfield pattern recognitionon a scale-free neural network[J]. The European Physical Journal B-Condensed Matter andComplex Systems,2003,32(3):395-399.
[10] L. G. Morelli, G. Abramson, M. N. Kuperman. Associative memory on a small-world neuralnetwork[J]. The European Physical Journal B-Condensed Matter and Complex Systems,2004,38(3):495-500.
[11] S. Zhang, G. Jin, X. S. Zhang, et al. Discovering functions and revealing mechanisms atmolecular level from biological networks[J]. Proteomics,2007,7(16):2856-2869.
[12] R. Guimera, L. A. N. Amaral. Cartography of complex networks: modules and universalroles[J]. Journal of Statistical Mechanics: Theory and Experiment,2005,2005(02):020-026.
[13] R. Guimera, L. A. N. Amaral. Functional cartography of complex metabolic networks[J].Nature,2005,433(7028):895-900.
[14] D. H. Zanette. Dynamics of rumor propagation on small-world networks[J]. PhysicalReview E,2002,65(4):041-048.
[15] Y. Moreno, M. Nekovee, A. F. Pacheco. Dynamics of rumor spreading in complexnetworks[J]. Physical Review E,2004,69(6):066-130.
[16] L. A. Meyers, B. Pourbohloul, M. E. Newman, et al. Network theory and SARS: predictingoutbreak diversity[J]. Journal of Theoretical Biology,2005,232(1):71-81.
[17] M. J. Keeling, K. T. Eames. Networks and epidemic models[J]. Journal of the Royal SocietyInterface,2005,2(4):295-307.
[18] V. Colizza, A. Barrat, M. Barthélemy, et al. The role of the airline transportation network inthe prediction and predictability of global epidemics[J]. Proceedings of the NationalAcademy of Sciences of the United States of America,2006,103(7):2015-2020.
[19] M. Faloutsos, P. Faloutsos, C. Faloutsos. On power-law relationships of the internettopology[J]. ACM SIGCOMM Computer Communication Review,1999,29(2):251-262.
[20] A. Vázquez, R. Pastor-Satorras, A. Vespignani. Large-scale topological and dynamicalproperties of the Internet[J]. Physical Review E,2002,65(6):066-130.
[21] R. Kinney, P. Crucitti, R. Albert, et al. Modeling cascading failures in the north americanpower grid[J]. The European Physical Journal B-Condensed Matter and Complex Systems,2005,46(1):101-107.
[22] R. Albert, I. Albert, G. L. Nakarado. Structural vulnerability of the north american powergrid[J]. Physical Review E,2004,69(2):025-103.
[23] R. Guimera, L. A. N. Amaral. Modeling the world-wide airport network[J]. The EuropeanPhysical Journal B-Condensed Matter and Complex Systems,2004,38(2):381-385.
[24] R. Guimera, S. Mossa, A. Turtschi, et al. The worldwide air transportation network:anomalous centrality, community structure, and cities' global roles[J]. Proceedings of theNational Academy of Sciences,2005,102(22):7794-7799.
[25] M. E. Newman, S. Forrest, J. Balthrop. Email networks and the spread of computerviruses[J]. Physical Review E,2002,66(3):035-101.
[26] J. Wang, P. De Wilde. Properties of evolving e-mail networks[J]. Physical Review E,2004,70(6):066-121.
[27]韩毅.社会网络分析与挖掘的若干关键问题研究[D].长沙:国防科学技术大学,2011,6-8.
[28] S. Wasserman, K. Faust. Social network analysis: methods and applications[M]. Cambridge:Cambridge University Press,1994,28-35.
[29] J.Scott. Social network analysis: a handbook[M]. London: Sage Publications,2000,113-152.
[30] S. J. South, D. L. Haynie. Friendship networks of mobile adolescents[J]. Social Forces,2004,83(1):315-350.
[31] M. E. Newman. Coauthorship networks and patterns of scientific collaboration[J].Proceedings of the National Academy of Sciences of the United States of America,2004,101(Suppl1):5200-5205.
[32] J. J. Ramasco, S. N. Dorogovtsev, R. Pastor-Satorras. Self-organization of collaborationnetworks[J]. Physical Review E,2004,70(3):036-106.
[33] R. Guimera, L. Danon, A. Diaz-Guilera, et al. Self-similar community structure in a networkof human interactions[J]. Physical Review E,2003,68(6):065-103.
[34] J.-P. Onnela, J. Saram ki, J. Hyv nen, et al. Structure and tie strengths in mobilecommunication networks[J]. Proceedings of the National Academy of Sciences,2007,104(18):7332-7336.
[35] E. A. Leicht, G. Clarkson, K. Shedden, et al. Large-scale structure of time evolving citationnetworks[J]. The European Physical Journal B-Condensed Matter and Complex Systems,2007,59(1):75-83.
[36] R. Pastor-Satorras, A. Vespignani. Evolution and structure of the Internet: a statisticalphysics approach[M]. Cambridge: Cambridge University Press,2007,20-35.
[37] L. A. Adamic, B. A. Huberman. Power-law distribution of the world wide web[J]. Science,2000,287(5461):2115-2115.
[38] J. Travers, S. Milgram. An experimental study of the small world problem[J]. Sociometry,1969,425-443.
[39] J. M. Kleinberg. Navigation in a small world[J]. Nature,2000,406(6798):845-845.
[40] M. E. Newman, S. H. Strogatz, D. J. Watts. Random graphs with arbitrary degreedistributions and their applications[J]. Physical Review E,2001,64(2):026-118.
[41] A. Fabrikant, E. Koutsoupias, C. Papadimitriou. Heuristically optimized trade-offs: A newparadigm for power laws in the Internet[J]. Automata, Languages and Programming,2002,14(5):781-781.
[42] J. Kleinberg, R. Kumar, P. Raghavan, et al. The web as a graph: measurements, models, andmethods[J]. Computing and Combinatorics,1999,10(3):1-17.
[43] L. Page, S. Brin, R. Motwani, et al. The pagerank citation ranking: bringing order to theweb[R]. San Francisco: Stanford InfoLab,1999.
[44] G. Pandurangan, P. Raghavan, E. Upfal. Using pagerank to characterize web structure[J].Computing and Combinatorics,2002,98(10):1-4.
[45] S. Redner. How popular is your paper? an empirical study of the citation distribution[J]. TheEuropean Physical Journal B-Condensed Matter and Complex Systems,1998,4(2):131-134.
[46] H. Tangmunarunkit, R. Govindan, S. Jamin, et al. Network topologies, power laws, andhierarchy[J]. ACM SIGCOMM Computer Communication Review,2002,32(1):76-85.
[47] M. Girvan, M. E. J. Newman. Community structure in social and biological networks[J]. theNational Academy of Sciences of the United States,2002,99(12):78-21.
[48] M. E. J. Newman, M. Girvan. Finding and evaluating community structure in networks[J].Physical Review E,2004,69(2):026-113.
[49] M. E. J. Newman. Modularity and community structure in networks[J]. the NationalAcademy of Sciences,2006,103(23):8577-8582.
[50] M. E. J. Newman. Fast algorithm for detecting community structure in networks[J].Physical Review E,2004,69(6):066-133.
[51] A. Clauset, M. E. J. Newman, C. Moore. Finding community structure in very largenetworks[J]. Physical Review E,2004,70(6):066-111.
[52] M. Boguná, R. Pastor-Satorras, A. Vespignani. Absence of epidemic threshold in scale-freenetworks with degree correlations[J]. Physical Review Letters,2003,90(2):28-701.
[53] S. Boccaletti, V. Latora, Y. Moreno, et al. Complex networks: structure and dynamics[J].Physics Reports,2006,424(4):175-308.
[54] M. E. J. Newman. The structure and function of complex networks[J]. SIAM Review,2003,45(2):167-256.
[55] M. E. Newman. Who is the best connected scientist? a study of scientific coauthorshipnetworks[J]. Complex Networks,2004,6(4):337-370.
[56]高霖.社会网络动态性及网络环境中的分布式搜索策略研究[D].合肥:中国科学技术大学,2009,20-23.
[57] D. J. Watts, S. H. Strogatz. Collective dynamics of ‘small-world’networks[J]. Nature,1998,393(6684):440-442.
[58] M. E. Newman, D. J. Watts. Renormalization group analysis of the small-world networkmodel[J]. Physics Letters A,1999,263(4):341-346.
[59] M. E. Newman, D. J. Watts. Scaling and percolation in the small-world network model[J].Physical Review E,1999,60(6):7332-7338.
[60] R. Kasturirangan. Multiple scales in small-world graphs[J]. Disordered Systems and NeuralNetworks1999,9(3):56-71.
[61] S. N. Dorogovtsev, J. F. F. Mendes. Exactly solvable small-world network[J]. EPL(Europhysics Letters),2000,50(1):161-167.
[62] A.-L. Barabási, R. Albert. Emergence of scaling in random networks[J]. Science,1999,286(5439):509-512.
[63] G. K. Zipf. Selected studies of the principle of relative frequency in language[M]. Oxford:Harvard Univ. Press,1932,20-37.
[64] W. J. Reed. The Pareto, Zipf and other power laws[J]. Economics Letters,2001,74(1):15-19.
[65] M. R. Schroeder. Fractals, chaos, power laws: minutes from an infinite paradise[M]. CourierDover Publications,2009,98-123.
[66] J. Scott. Social network analysis[J]. Sociology,1988,22(1):109-127.
[67] P. M. Gleiser, L. Danon. Community structure in jazz[J]. Advances in Complex Systems,2003,6(04):565-573.
[68] J. R. Tyler, D. M. Wilkinson, B. A. Huberman. E-mail as spectroscopy: automated discoveryof community structure within organizations[J]. The Information Society,2005,21(2):143-153.
[69]赖大荣.复杂网络社团结构分析方法研究[D].上海:上海交通大学,2011,13-15.
[70] G. W. Flake, S. Lawrence, C. L. Giles, et al. Self-organization and identification of webcommunities[J]. Computer,2002,35(3):66-70.
[71] V. D. Blondel, J. L. Guillaume, R. Lambiotte, et al. Fast unfolding of communities in largenetworks[J]. Journal of Statistical Mechanics: Theory and Experiment,2008,2008(10):108-130.
[72] P. Holme, M. Huss, H. Jeong. Subnetwork hierarchies of biochemical pathways[J].Bioinformatics,2003,19(4):532-538.
[73] G. D. Bader, C. W. Hogue. An automated method for finding molecular complexes in largeprotein interaction networks[J]. BMC Bioinformatics,2003,4(1):2-5.
[74] D. M. Wilkinson, B. A. Huberman. A method for finding communities of related genes[J].Proceedings of the National Academy of Sciences of the United States of America,2004,101(Suppl1):5241-5248.
[75] G. Jin, S. Zhang, X.-S. Zhang, et al. Hubs with network motifs organize modularitydynamically in the protein-protein interaction network of yeast[J]. PLoS One,2007,2(11):1207-1211.
[76] M. R. Anderberg. Cluster analysis for applications[R]. DTIC Document,1973.
[77] B. Kernighan, S. Lin. An eflicient heuristic procedure for partitioning graphs[J]. BellSystem Technical Journal,1970,3(1):670-677.
[78] M. Fiedler. Algebraic connectivity of graphs[J]. Czechoslovak Mathematical Journal,1973,23(2):298-305.
[79] A. Pothen, H. D. Simon, K. P. Liou. Partitioning sparse matrices with eigenvectors ofgraphs[J]. SIAM Journal on Matrix Analysis and Applications,1990,11(3):430-452.
[80] M. Fiedler. Algebraic connectivity of graphs[J]. Czechoslovak Mathematical Journal,1973,23(98):298-305.
[81]戈卢布,范,洛恩, et al.矩阵计算[M].北京:科学出版社,2001,87-115.
[82] M. E. J. Newman. A measure of betweenness centrality based on random walks[J]. SocialNetworks,2005,27(1):39-54.
[83] F. Radicchi, C. Castellano, F. Cecconi, et al. Defining and identifying communities innetworks[J]. the National Academy of Sciences of the United States,2004,101(9):26-58.
[84] H. Balakrishnan, N. Deo. Discovering communities in complex networks[C]. Proceedingsof the44th Annual Southeast Regional Conference, Melbourne,2006,280-285.
[85] R. S. Burt. Positions in networks[J]. Social Forces,1976,55(1):93-122.
[86] A. Broder, R. Kumar, F. Maghoul, et al. Graph structure in the web[J]. Computer Networks,2000,33(1):309-320.
[87] J. Leskovec, K. J. Lang, A. Dasgupta, et al. Community structure in large networks: naturalcluster sizes and the absence of large well-defined clusters[J]. Internet Mathematics,2009,6(1):29-123.
[88] D. Chakrabarti, R. Kumar, A. Tomkins. Evolutionary clustering[C]. Proceedings of the12thACM SIGKDD International Conference on Knowledge Discovery and Data Mining,Philadelphia,2006,554-560.
[89] Y.-R. Lin, Y. Chi, S. Zhu, et al. Facetnet: a framework for analyzing communities and theirevolutions in dynamic networks[C]. Proceedings of the17th International Conference onWorld Wide Web, Beijing,2008,685-694.
[90]单波,姜守旭,张硕, et al. IC:动态社会关系网络社区结构的增量识别算法[J]. Journalof Software,2009,20(3):184-192.
[91]谈杰,李星.网络测量综述[J].计算机应用研究,2006,23(2):5-8.
[92]张宏莉,方滨兴,胡铭曾, et al. Internet测量与分析综述[J].软件学报,2003,14(1):110-116.
[93]田茂泰. P2P网络性能监测研究与实现[D].贵州:贵州大学,2008,67-82.
[94] J. Chen, B. Yuan. Detecting functional modules in the yeast protein–protein interactionnetwork[J]. Bioinformatics,2006,22(18):2283-2290.
[95] S. Fortunato, V. Latora, M. Marchiori. Method to find community structures based oninformation centrality[J]. Physical Review E,2004,70(5):056-104.
[96] S. Gregory. An algorithm to find overlapping community structure in networks[C].Knowledge Discovery in Databases: PKDD2007, Warsaw,2007,91-102.
[97] L. Danon, A. Díaz-Guilera, A. Arenas. The effect of size heterogeneity on communityidentification in complex networks[J]. Journal of Statistical Mechanics: Theory andExperiment,2006,2006(11):110-124.
[98] K. Wakita, T. Tsurumi. Finding community structure in mega-scale socialnetworks:[extended abstract][C]. Proceedings of the16th International Conference on WorldWide Web, Banff,2007,1275-1276.
[99] P. Schuetz, A. Caflisch. Efficient modularity optimization by multistep greedy algorithm andvertex mover refinement[J]. Physical Review E,2008,77(4):046-112.
[100] P. Schuetz, A. Caflisch. Multistep greedy algorithm identifies community structure inreal-world and computer-generated networks[J]. Physical Review E,2008,78(2):026-112.
[101] J. M. Pujol, J. Béjar, J. Delgado. Clustering algorithm for determining community structurein large networks[J]. Physical Review E,2006,74(1):016-107.
[102] H. Du, M. W. Feldman, S. Li, et al. An algorithm for detecting community structure ofsocial networks based on prior knowledge and modularity[J]. Complexity,2007,12(3):53-60.
[103] M. E. J. Newman. Analysis of weighted networks[J]. Physical Review E,2004,70(5):056-131.
[104] L. Donetti, M. A. Munoz. Detecting network communities: a new systematic and efficientalgorithm[J]. Journal of Statistical Mechanics: Theory and Experiment,2004,2004(10):100-122.
[105] J. Duch, A. Arenas. Community detection in complex networks using extremaloptimization[J]. Physical Review E,2005,72(2):027-104.
[106] S. Fortunato. Community detection in graphs[J]. Physics Reports,2010,486(3):75-174.
[107] W. W. Zachary. An information flow model for conflict and fission in small groups[J].Journal of Anthropological Research,1977,4(2):452-473.
[108] H. Liu, X. Qin, H. Yun, et al. A community detecting algorithm in directed weightednetworks[J]. Electrical Engineering and Control,2011,98(3):11-17.
[109] P. Eades, Q.-W. Feng. Multilevel visualization of clustered graphs[C]. Graph Drawing,Rome,1997,101-112.
[110]吴鹏,李思昆.适于社会网络结构分析与可视化的布局算法[J].软件学报,2011,22(10):63-70.
[111] Y. Hu. Algorithms for visualizing large networks[J]. Combinatorial Scientific Computing (toappear),2011,5(3):180-186.
[112] G. Palla, A.-L. Barabási, T. Vicsek. Quantifying social group evolution[J]. Nature,2007,446(7136):664-667.
[113] M. Rosvall, C. T. Bergstrom. Mapping change in large networks[J]. PLoS One,2010,5(1):86-94.
[114]熊站营.基于增量和密度的动态网络社团检测算法[D].西安电子科技大学,2012,20-31.
[115] C. Tantipathananandh, T. Berger-Wolf, D. Kempe. A framework for communityidentification in dynamic social networks[C]. Proceedings of the13th ACM SIGKDDInternational Conference on Knowledge Discovery and Data Mining, San Jose,2007,717-726.
[116] M.-S. Kim, J. Han. A particle-and-density based evolutionary clustering method fordynamic networks[J]. Proceedings of the VLDB Endowment,2009,2(1):622-633.
[117] G. Palla, A.-L. Barabasi, T. Vicsek. Quantifying social group evolution[J]. Nature,2007,446(7136):664-667.
[118] S. Asur, S. Parthasarathy. A viewpoint-based approach for interaction graph analysis[C].Proceedings of the15th ACM SIGKDD International Conference on Knowledge Discoveryand Data Mining, Paris,2009,79-88.
[119] Z. Feng, X. Xu, N. Yuruk, et al. A novel similarity-based modularity function for graphpartitioning[J]. Data Warehousing and Knowledge Discovery,2007,3(2):385-396.
[120] H. W. Kuhn. The hungarian method for the assignment problem[J]. Naval ResearchLogistics Quarterly,2006,2(1-2):83-97.
[121] R. Guimerà, M. Sales-Pardo, L. A. Amaral. Classes of complex networks defined byrole-to-role connectivity profiles[J]. Nature Physics,2006,3(1):63-69.
[122] R. Milo, S. Itzkovitz, N. Kashtan, et al. Superfamilies of evolved and designed networks[J].Science,2004,303(5663):1538-1542.
[123] J. P. Bagrow, E. M. Bollt, J. D. Skufca, et al. Portraits of complex networks[J]. EPL(Europhysics Letters),2008,81(6):68-84.
[124] M. Middendorf, E. Ziv, C. H. Wiggins. Inferring network mechanisms: the drosophilamelanogaster protein interaction network[J]. Proceedings of the National Academy ofSciences of the United States of America,2005,102(9):3192-3197.
[125] E. Estrada. Virtual identification of essential proteins within the protein interaction networkof yeast[J]. Proteomics,2006,6(1):35-40.
[126] D. Gomez, E. González-Arangüena, C. Manuel, et al. Centrality and power in socialnetworks: a game theoretic approach[J]. Mathematical Social Sciences,2003,46(1):27-54.
[127]刘军.社会网络分析导论[M].北京:社会科学文献出版社,2004,19-31.
[128]罗家德.社会网络分析讲义[M].北京:社会科学文献出版社,2005,168-200.
[129] L. C. Freeman. A set of measures of centrality based on betweenness[J]. Sociometry,1977,8(2):35-41.
[130] P. Bonacich. Factoring and weighting approaches to status scores and cliqueidentification[J]. Journal of Mathematical Sociology,1972,2(1):113-120.
[131] P. Bonacich. Power and centrality: a family of measures[J]. American Journal of Sociology,1987,4(6):1170-1182.
[132] R. I. Dunbar. Coevolution of neocortical size, group size and language in humans[J].Behavioral and Brain Sciences,1993,16(4):681-693.
[133] à. Arenas, A. Cabrales, A. Díaz-Guilera, et al. Optimal information transmission inorganizations: search and congestion[J]. Review of Economic Design,2003,14(1-2):75-93.
[134] A. Barrat, M. Barthelemy, R. Pastor-Satorras, et al. The architecture of complex weightednetworks[J]. the National Academy of Sciences of the United States of America,2004,101(11):3747-3752.
[135] M. Latapy. Main-memory triangle computations for very large (sparse (power-law))graphs[J]. Theoretical Computer Science,2008,407(1):458-473.
[136] L. A. N. Amaral, A. Scala, M. Barthélémy, et al. Classes of small-world networks[J].Proceedings of the National Academy of Sciences,2000,97(21):11149-11152.
[137] E. Ravasz, A. L. Barabási. Hierarchical organization in complex networks[J]. PhysicalReview E,2003,67(2):026-112.
[138] M. E. J. Newman. Assortative mixing in networks[J]. Physical Review Letters,2002,89(20):208-701.
[139]张杰. P2P系统中激励相容的机制设计与实现[D].天津:天津大学,2007,24-39.
[140]李江涛.对等网络性能测量与改善[D].北京:北京邮电大学,2006,81-94.
[141]刘维光,陈立伟.一种基于DHT的P2P搜索方法[J].微计算机信息,2006,3(1):131-133.
[142]杨天路. P2P网络技术原理与系统开发案例[M].人民邮电出版社,2007,56-75.
[143]蒋雪玲. P2P流媒体系统多点下载技术的实现研究[J].沈阳大学学报,2006,18(4):63-66.
[144]汪燕,柳斌. BitTorrent协议分析及控制策略[J].实验技术与管理,2006,23(1):54-56.
[145]邓罡.面向网络媒体播放器（IMP）的嵌入式系统的研究与实现[D].长沙:湖南师范大学,2007,16-32.
[146] A. Legout, G. Urvoy-Keller, P. Michiardi. Understanding bittorrent: an experimentalperspective[J]. Research Institute at the Heart of the Information Society,2005,3(2):66-74.
[147] L. Rong, I. Burnett. BitTorrent in a dynamic resource adapting peer-to-peer network[C].First International Conference on Automated Production of Cross Media Content forMulti-Channel Distribution，Washington,2005,69-73.
[148] A. Asvanund, S. Bagla, M. Kapadia, et al. Intelligent club management in peer-to-peernetworks[C]. Proceedings of First Workshop on Economics of P2P, Berkeley,2003,45-52.
[149] J. Li, D. Huang, J. Huang, et al. An extension of HAVE message in BitTorrent systems[C].International Conference on Communication Technology, Tsukuba,2006,1-4.
[150] M. Ripeanu, I. Foster, A. Iamnitchi. Mapping the gnutella network: properties of large-scalepeer-to-peer systems and implications for system design[J]. Distributed, Parallel, andCluster Computing2002,6(8):632-636.
[151] S. Saroiu, P. K. Gummadi, S. D. Gribble. Measurement study of peer-to-peer file sharingsystems[C]. Electronic Imaging2002, San Jose,2002,156-170.
[152] D. Stutzbach, R. Rejaie. Towards a better understanding of churn in peer-to-peernetworks[R]. Oregon: Univ. of Oregon,2004.
[153]刘琼,徐鹏,杨海涛, et al. Peer-to-Peer文件共享系统的测量研究[J].软件学报,2006,17(10):2131-2140.
[154] S. Sen, J. Wang. Analyzing peer-to-peer traffic across large networks[J]. IEEE/ACMTransactions on Networking (ToN),2004,12(2):219-232.
[155] M. Andreolini, R. Lancellotti, P. S. Yu. Analysis of peer-to-peer systems: workloadcharacterization and effects on traffic cacheability[C]. Proceedings of the IEEE ComputerSociety's12th Annual International Symposium on Modeling, Analysis, and Simulation ofComputer and Telecommunications System, Volendam,2004,95-104.
[156] N. Leibowitz, M. Ripeanu, A. Wierzbicki. Deconstructing the kazaa network[C].Proceedings of the Third IEEE Workshop on Internet Applications, San Jose,2003,112-120.
[157] W. Wang, H. Chang, A. Zeitoun, et al. Characterizing guarded hosts in peer-to-peer filesharing systems[C]. Global Telecommunications Conference, Dallas,2004,1539-1543.
[158] D. S. Wallach. A survey of peer-to-peer security issues[J]. Software Security-Theories andSystems,2003,9(3):42-57.
[159] M. Castro, P. Druschel, A. Ganesh, et al. Secure routing for structured peer-to-peer overlaynetworks[J]. ACM SIGOPS Operating Systems Review,2002,36(SI):299-314.
[160] E. Sit, R. Morris. Security considerations for peer-to-peer distributed hash tables[J].Peer-to-Peer Systems,2002,11(2):261-269.
[161] Y. Wang, J. Vassileva. Trust and reputation model in peer-to-peer networks[C]. Proceedingsof third International Conference onPeer-to-Peer Computing, Netherlands,2003,150-157.
[162] S. Bellovin. Security aspects of Napster and Gnutella[C].2001Usenix Annual TechnicalConference, Boston,2001,306-311.
[163] S. Hazel, B. Wiley. Achord: a variant of the chord lookup service for use in censorshipresistant peer-to-peerpublishing systems[C]. Proceedings of the1st International Workshopon Peer-to-Peer Systems (IPTPS’02), Cambridge,2002,34-39.
[164] A. Serjantov. Anonymizing censorship resistant systems[J]. Peer-to-Peer Systems,2002,4(2):111-120.
[165] T. Murphy, T. M. Vii, A. K. Manjhi. Anonymous identity and trust for peer-to-peernetworks[J]. CiteSeer,2002,5(1):20-26.
[166] A. K. Datta, M. Gradinariu, M. Raynal, et al. Anonymous publish/subscribe in p2pnetworks[C]. Proceedings of International Parallel and Distributed Processing Symposium,Nice,2003,8-13.
[167] V. Sadafal. Measurement and analysis of BitTorrent[D]. College Station: Texas A&MUniversity,2008,36-52.
[168] J. R. Douceur. The sybil attack[J]. Peer-to-Peer Systems,2002,8(2):251-260.
[169] D. Xuan, S. Chellappan, X. Wang. Resilience of structured peer to peer systems: Analysisand enhancement[J]. Handbook On Theoretical and Algorithmic Aspects Of Sensor, Ad HocWireless, and Peer-to-Peer Networks,2005,6(2):767-786.
[170] K. Gummadi, R. Gummadi, S. Gribble, et al. The impact of DHT routing geometry onresilience and proximity[C]. Proceedings of the2003Conference on Applications,Technologies, Architectures, and Protocols for Computer Communications, Karlsruhe,2003,381-394.
[171] L. Massoulié, A.-M. Kermarrec, A. J. Ganesh. Network awareness and failure resilience inself-organizing overlay networks[C]. Proceedings of the22nd International Symposium onReliable Distributed Systems, Florence,2003,47-55.
[172] J. Aspnes, Z. Diamadi, G. Shah. Fault-tolerant routing in peer-to-peer systems[C].Proceedings of the twenty-first Annual Symposium on Principles of Distributed Computing,Monterey,2002,223-232.
[173] S. Wang, D. Xuan, W. Zhao. On resilience of structured peer-to-peer systems[C]. GlobalTelecommunications Conference, San Francisco,2003,3851-3856.
[174] D. Loguinov, A. Kumar, V. Rai, et al. Graph-theoretic analysis of structured peer-to-peersystems: routing distances and fault resilience[C]. Proceedings of the2003Conference onApplications, Technologies, Architectures, and Protocols for Computer Communications,Karlsruhe,2003,395-406.
[175] H. Balakrishnan, M. F. Kaashoek, D. Karger, et al. Looking up data in P2P systems[J].Communications of the ACM,2003,46(2):43-48.
[176] B. Gedik, L. Liu. Reliable peer-to-peer information monitoring through replication[C].Proceedings of the22nd International Symposium on Reliable Distributed Systems,Florence,2003,56-65.
[177] J. Risson, T. Moors. Survey of research towards robust peer-to-peer networks: searchmethods[J]. Computer Networks,2006,50(17):3485-3521.
[178] J. Avnet, J. Saia. Towards robust and scalable trust metrics[C].47th Annual IEEESymposium on Foundations of Computer Science, Longdon,2003,196-202.
[179] M. Feldman, K. Lai, I. Stoica, et al. Robust incentive techniques for peer-to-peernetworks[C]. Proceedings of the5th ACM Conference on Electronic Commerce, New York,2004,102-111.
[180] A. Stavrou, D. Rubenstein, S. Sahu. A lightweight, robust P2P system to handle flashcrowds[J]. Selected Areas in Communications, IEEE Journal on,2004,22(1):6-17.
[181] D. Rubenstein, S. Sahu. An analysis of a simple p2p protocol for flash crowd documentretrieval[R]. New York: Columbia University,2001.
[182] M. E. Renda, J. Callan. The robustness of content-based search in hierarchical peer to peernetworks[C]. Proceedings of the thirteenth ACM International Conference on Informationand Knowledge Management, Washington,2004,562-570.
[183] S. Dorogovtsev. Clustering of correlated networks[J]. Phys. Rev. E,2003,69(2):027-104.
[184] S. Zhou, R. J. Mondragn. Accurately modeling the Internet topology[J]. Physical Review E,2004,70(6):066-108.
[185]张国强,张国清. Internet网络的关联性研究[J].软件学报,2006,17(3):490-497.
[186] L. Ramaswamy, B. Gedik, L. Liu. A distributed approach to node clustering in decentralizedpeer-to-peer networks[J]. IEEE Transactions on Parallel and Distributed Systems,2005,5(4):814-829.
[187] F. Le Fessant, S. Handurukande, A. M. Kermarrec, et al. Clustering in peer-to-peer filesharing workloads[J]. Peer-to-Peer Systems III,2005,51(6):217-226.
[188] F. Cantin, B. Gueye, M. A. Kaafar, et al. A self-organized clustering scheme for overlaynetworks[J]. Self-Organizing Systems,2008,7(3):59-70.
[189] J. L. Guillaume, S. Le Blond. Clustering in P2P exchanges[J]. AlgoTel2005SeptiemesRencontres Francopohones Sur Les Aspects Algorithmiques Des Telecommunications,2005,2(4):81-86.
[190] S. B. Handurukande, A. M. Kermarrec, F. L. Fessant, et al. Exploiting semantic clustering inthe edonkey p2p network[C]. Proceedings of the11th Workshop on ACM SIGOPSEuropean Workshop, New York,2004,20-26.
[191] M. Izal, G. Urvoy-Keller, E. W. Biersack, et al. Dissecting bittorrent: five months in atorrent’s lifetime[J]. Passive and Active Network Measurement,2004,11(3):1-11.
[192] B. Krishnamurthy, J. Wang, Y. Xie. Early measurements of a cluster-based architecture forP2P systems[C]. Proceedings of the1st ACM SIGCOMM Workshop on InternetMeasurement, San Francisco,2001,105-109.
[193] E. Adar, B. A. Huberman. Free riding on gnutella[J]. First Monday,2000,5(10-2):56-69.
[194] C. Fraleigh, S. Moon, B. Lyles, et al. Packet-level traffic measurements from the sprint IPbackbone[J]. Network, IEEE,2003,17(6):6-16.
[195] S. Sen, O. Spatscheck, D. Wang. Accurate, scalable in-network identification of p2p trafficusing application signatures[C]. Proceedings of the13th International Conference on WorldWide Web, New York,2004,512-521.
[196] L. A. Adamic, R. M. Lukose, A. R. Puniyani, et al. Search in power-law networks[J].Physical Review E,2001,64(4):046-135.
[197] Q. Lv, P. Cao, E. Cohen, et al. Search and replication in unstructured peer-to-peernetworks[C]. Proceedings of the16th International Conference on Supercomputing, NewYork,2002,84-95.
[198] M. Jovanovi, F. Annexstein, K. Berman. Modeling peer-to-peer network topologiesthrough small-world models and power laws[C]. IX Telecommunications Forum Telfor,Belgrade,2001,1-4.
[199] D. Stutzbach, R. Rejaie, S. Sen. Characterizing unstructured overlay topologies in modernP2P file-sharing systems[J]. Networking, IEEE/ACM Transactions on,2008,16(2):267-280.
[200] D. Stutzbach, R. Rejaie. Capturing accurate snapshots of the Gnutella network[C].Proceedings IEEE INFOCOM2005.24th Annual Joint Conference of the IEEE Computerand Communications Societies, Miami,2005,2825-2830.
[201]郑翔平. BT swarm网络的主动测量与时序性分析[D].成都:电子科技大学,2011,35-47.
[202]李洋. BitTorrent对等网络主动测量研究[D].成都:电子科技大学,2011,80-94.
[2] A. L. Barabási, R. Albert. Emergence of scaling in random networks[J]. Science,1999,286(5439):509-512.
[3] S. H. Strogatz. Exploring complex networks[J]. Nature,2001,410(6825):268-276.
[4]杨树忠.复杂网络中的社团检测问题研究[D].北京:北京交通大学,2009,3-4.
[5] D. Simard, L. Nadeau, H. Kr ger. Fastest learning in small-world neural networks[J].Physics Letters A,2005,336(1):8-15.
[6] J. J. Torres, M. A. Munoz, J. Marro, et al. Influence of topology on the performance of aneural network[J]. Neurocomputing,2004,58(6):229-234.
[7] S. Yang, S. Luo, J. Li. Building multi-layer small world neural network[J]. Advances inNeural Networks,2006,10(1):695-700.
[8] S. Yang, S. Luo, J. Li. An extended model on self-organizing map[J]. Neural InformationProcessing,2006,6(2):987-994.
[9] D. Stauffer, A. Aharony, L. da Fontoura Costa, et al. Efficient hopfield pattern recognitionon a scale-free neural network[J]. The European Physical Journal B-Condensed Matter andComplex Systems,2003,32(3):395-399.
[10] L. G. Morelli, G. Abramson, M. N. Kuperman. Associative memory on a small-world neuralnetwork[J]. The European Physical Journal B-Condensed Matter and Complex Systems,2004,38(3):495-500.
[11] S. Zhang, G. Jin, X. S. Zhang, et al. Discovering functions and revealing mechanisms atmolecular level from biological networks[J]. Proteomics,2007,7(16):2856-2869.
[12] R. Guimera, L. A. N. Amaral. Cartography of complex networks: modules and universalroles[J]. Journal of Statistical Mechanics: Theory and Experiment,2005,2005(02):020-026.
[13] R. Guimera, L. A. N. Amaral. Functional cartography of complex metabolic networks[J].Nature,2005,433(7028):895-900.
[14] D. H. Zanette. Dynamics of rumor propagation on small-world networks[J]. PhysicalReview E,2002,65(4):041-048.
[15] Y. Moreno, M. Nekovee, A. F. Pacheco. Dynamics of rumor spreading in complexnetworks[J]. Physical Review E,2004,69(6):066-130.
[16] L. A. Meyers, B. Pourbohloul, M. E. Newman, et al. Network theory and SARS: predictingoutbreak diversity[J]. Journal of Theoretical Biology,2005,232(1):71-81.
[17] M. J. Keeling, K. T. Eames. Networks and epidemic models[J]. Journal of the Royal SocietyInterface,2005,2(4):295-307.
[18] V. Colizza, A. Barrat, M. Barthélemy, et al. The role of the airline transportation network inthe prediction and predictability of global epidemics[J]. Proceedings of the NationalAcademy of Sciences of the United States of America,2006,103(7):2015-2020.
[19] M. Faloutsos, P. Faloutsos, C. Faloutsos. On power-law relationships of the internettopology[J]. ACM SIGCOMM Computer Communication Review,1999,29(2):251-262.
[20] A. Vázquez, R. Pastor-Satorras, A. Vespignani. Large-scale topological and dynamicalproperties of the Internet[J]. Physical Review E,2002,65(6):066-130.
[21] R. Kinney, P. Crucitti, R. Albert, et al. Modeling cascading failures in the north americanpower grid[J]. The European Physical Journal B-Condensed Matter and Complex Systems,2005,46(1):101-107.
[22] R. Albert, I. Albert, G. L. Nakarado. Structural vulnerability of the north american powergrid[J]. Physical Review E,2004,69(2):025-103.
[23] R. Guimera, L. A. N. Amaral. Modeling the world-wide airport network[J]. The EuropeanPhysical Journal B-Condensed Matter and Complex Systems,2004,38(2):381-385.
[24] R. Guimera, S. Mossa, A. Turtschi, et al. The worldwide air transportation network:anomalous centrality, community structure, and cities' global roles[J]. Proceedings of theNational Academy of Sciences,2005,102(22):7794-7799.
[25] M. E. Newman, S. Forrest, J. Balthrop. Email networks and the spread of computerviruses[J]. Physical Review E,2002,66(3):035-101.
[26] J. Wang, P. De Wilde. Properties of evolving e-mail networks[J]. Physical Review E,2004,70(6):066-121.
[27]韩毅.社会网络分析与挖掘的若干关键问题研究[D].长沙:国防科学技术大学,2011,6-8.
[28] S. Wasserman, K. Faust. Social network analysis: methods and applications[M]. Cambridge:Cambridge University Press,1994,28-35.
[29] J.Scott. Social network analysis: a handbook[M]. London: Sage Publications,2000,113-152.
[30] S. J. South, D. L. Haynie. Friendship networks of mobile adolescents[J]. Social Forces,2004,83(1):315-350.
[31] M. E. Newman. Coauthorship networks and patterns of scientific collaboration[J].Proceedings of the National Academy of Sciences of the United States of America,2004,101(Suppl1):5200-5205.
[32] J. J. Ramasco, S. N. Dorogovtsev, R. Pastor-Satorras. Self-organization of collaborationnetworks[J]. Physical Review E,2004,70(3):036-106.
[33] R. Guimera, L. Danon, A. Diaz-Guilera, et al. Self-similar community structure in a networkof human interactions[J]. Physical Review E,2003,68(6):065-103.
[34] J.-P. Onnela, J. Saram ki, J. Hyv nen, et al. Structure and tie strengths in mobilecommunication networks[J]. Proceedings of the National Academy of Sciences,2007,104(18):7332-7336.
[35] E. A. Leicht, G. Clarkson, K. Shedden, et al. Large-scale structure of time evolving citationnetworks[J]. The European Physical Journal B-Condensed Matter and Complex Systems,2007,59(1):75-83.
[36] R. Pastor-Satorras, A. Vespignani. Evolution and structure of the Internet: a statisticalphysics approach[M]. Cambridge: Cambridge University Press,2007,20-35.
[37] L. A. Adamic, B. A. Huberman. Power-law distribution of the world wide web[J]. Science,2000,287(5461):2115-2115.
[38] J. Travers, S. Milgram. An experimental study of the small world problem[J]. Sociometry,1969,425-443.
[39] J. M. Kleinberg. Navigation in a small world[J]. Nature,2000,406(6798):845-845.
[40] M. E. Newman, S. H. Strogatz, D. J. Watts. Random graphs with arbitrary degreedistributions and their applications[J]. Physical Review E,2001,64(2):026-118.
[41] A. Fabrikant, E. Koutsoupias, C. Papadimitriou. Heuristically optimized trade-offs: A newparadigm for power laws in the Internet[J]. Automata, Languages and Programming,2002,14(5):781-781.
[42] J. Kleinberg, R. Kumar, P. Raghavan, et al. The web as a graph: measurements, models, andmethods[J]. Computing and Combinatorics,1999,10(3):1-17.
[43] L. Page, S. Brin, R. Motwani, et al. The pagerank citation ranking: bringing order to theweb[R]. San Francisco: Stanford InfoLab,1999.
[44] G. Pandurangan, P. Raghavan, E. Upfal. Using pagerank to characterize web structure[J].Computing and Combinatorics,2002,98(10):1-4.
[45] S. Redner. How popular is your paper? an empirical study of the citation distribution[J]. TheEuropean Physical Journal B-Condensed Matter and Complex Systems,1998,4(2):131-134.
[46] H. Tangmunarunkit, R. Govindan, S. Jamin, et al. Network topologies, power laws, andhierarchy[J]. ACM SIGCOMM Computer Communication Review,2002,32(1):76-85.
[47] M. Girvan, M. E. J. Newman. Community structure in social and biological networks[J]. theNational Academy of Sciences of the United States,2002,99(12):78-21.
[48] M. E. J. Newman, M. Girvan. Finding and evaluating community structure in networks[J].Physical Review E,2004,69(2):026-113.
[49] M. E. J. Newman. Modularity and community structure in networks[J]. the NationalAcademy of Sciences,2006,103(23):8577-8582.
[50] M. E. J. Newman. Fast algorithm for detecting community structure in networks[J].Physical Review E,2004,69(6):066-133.
[51] A. Clauset, M. E. J. Newman, C. Moore. Finding community structure in very largenetworks[J]. Physical Review E,2004,70(6):066-111.
[52] M. Boguná, R. Pastor-Satorras, A. Vespignani. Absence of epidemic threshold in scale-freenetworks with degree correlations[J]. Physical Review Letters,2003,90(2):28-701.
[53] S. Boccaletti, V. Latora, Y. Moreno, et al. Complex networks: structure and dynamics[J].Physics Reports,2006,424(4):175-308.
[54] M. E. J. Newman. The structure and function of complex networks[J]. SIAM Review,2003,45(2):167-256.
[55] M. E. Newman. Who is the best connected scientist? a study of scientific coauthorshipnetworks[J]. Complex Networks,2004,6(4):337-370.
[56]高霖.社会网络动态性及网络环境中的分布式搜索策略研究[D].合肥:中国科学技术大学,2009,20-23.
[57] D. J. Watts, S. H. Strogatz. Collective dynamics of ‘small-world’networks[J]. Nature,1998,393(6684):440-442.
[58] M. E. Newman, D. J. Watts. Renormalization group analysis of the small-world networkmodel[J]. Physics Letters A,1999,263(4):341-346.
[59] M. E. Newman, D. J. Watts. Scaling and percolation in the small-world network model[J].Physical Review E,1999,60(6):7332-7338.
[60] R. Kasturirangan. Multiple scales in small-world graphs[J]. Disordered Systems and NeuralNetworks1999,9(3):56-71.
[61] S. N. Dorogovtsev, J. F. F. Mendes. Exactly solvable small-world network[J]. EPL(Europhysics Letters),2000,50(1):161-167.
[62] A.-L. Barabási, R. Albert. Emergence of scaling in random networks[J]. Science,1999,286(5439):509-512.
[63] G. K. Zipf. Selected studies of the principle of relative frequency in language[M]. Oxford:Harvard Univ. Press,1932,20-37.
[64] W. J. Reed. The Pareto, Zipf and other power laws[J]. Economics Letters,2001,74(1):15-19.
[65] M. R. Schroeder. Fractals, chaos, power laws: minutes from an infinite paradise[M]. CourierDover Publications,2009,98-123.
[66] J. Scott. Social network analysis[J]. Sociology,1988,22(1):109-127.
[67] P. M. Gleiser, L. Danon. Community structure in jazz[J]. Advances in Complex Systems,2003,6(04):565-573.
[68] J. R. Tyler, D. M. Wilkinson, B. A. Huberman. E-mail as spectroscopy: automated discoveryof community structure within organizations[J]. The Information Society,2005,21(2):143-153.
[69]赖大荣.复杂网络社团结构分析方法研究[D].上海:上海交通大学,2011,13-15.
[70] G. W. Flake, S. Lawrence, C. L. Giles, et al. Self-organization and identification of webcommunities[J]. Computer,2002,35(3):66-70.
[71] V. D. Blondel, J. L. Guillaume, R. Lambiotte, et al. Fast unfolding of communities in largenetworks[J]. Journal of Statistical Mechanics: Theory and Experiment,2008,2008(10):108-130.
[72] P. Holme, M. Huss, H. Jeong. Subnetwork hierarchies of biochemical pathways[J].Bioinformatics,2003,19(4):532-538.
[73] G. D. Bader, C. W. Hogue. An automated method for finding molecular complexes in largeprotein interaction networks[J]. BMC Bioinformatics,2003,4(1):2-5.
[74] D. M. Wilkinson, B. A. Huberman. A method for finding communities of related genes[J].Proceedings of the National Academy of Sciences of the United States of America,2004,101(Suppl1):5241-5248.
[75] G. Jin, S. Zhang, X.-S. Zhang, et al. Hubs with network motifs organize modularitydynamically in the protein-protein interaction network of yeast[J]. PLoS One,2007,2(11):1207-1211.
[76] M. R. Anderberg. Cluster analysis for applications[R]. DTIC Document,1973.
[77] B. Kernighan, S. Lin. An eflicient heuristic procedure for partitioning graphs[J]. BellSystem Technical Journal,1970,3(1):670-677.
[78] M. Fiedler. Algebraic connectivity of graphs[J]. Czechoslovak Mathematical Journal,1973,23(2):298-305.
[79] A. Pothen, H. D. Simon, K. P. Liou. Partitioning sparse matrices with eigenvectors ofgraphs[J]. SIAM Journal on Matrix Analysis and Applications,1990,11(3):430-452.
[80] M. Fiedler. Algebraic connectivity of graphs[J]. Czechoslovak Mathematical Journal,1973,23(98):298-305.
[81]戈卢布,范,洛恩, et al.矩阵计算[M].北京:科学出版社,2001,87-115.
[82] M. E. J. Newman. A measure of betweenness centrality based on random walks[J]. SocialNetworks,2005,27(1):39-54.
[83] F. Radicchi, C. Castellano, F. Cecconi, et al. Defining and identifying communities innetworks[J]. the National Academy of Sciences of the United States,2004,101(9):26-58.
[84] H. Balakrishnan, N. Deo. Discovering communities in complex networks[C]. Proceedingsof the44th Annual Southeast Regional Conference, Melbourne,2006,280-285.
[85] R. S. Burt. Positions in networks[J]. Social Forces,1976,55(1):93-122.
[86] A. Broder, R. Kumar, F. Maghoul, et al. Graph structure in the web[J]. Computer Networks,2000,33(1):309-320.
[87] J. Leskovec, K. J. Lang, A. Dasgupta, et al. Community structure in large networks: naturalcluster sizes and the absence of large well-defined clusters[J]. Internet Mathematics,2009,6(1):29-123.
[88] D. Chakrabarti, R. Kumar, A. Tomkins. Evolutionary clustering[C]. Proceedings of the12thACM SIGKDD International Conference on Knowledge Discovery and Data Mining,Philadelphia,2006,554-560.
[89] Y.-R. Lin, Y. Chi, S. Zhu, et al. Facetnet: a framework for analyzing communities and theirevolutions in dynamic networks[C]. Proceedings of the17th International Conference onWorld Wide Web, Beijing,2008,685-694.
[90]单波,姜守旭,张硕, et al. IC:动态社会关系网络社区结构的增量识别算法[J]. Journalof Software,2009,20(3):184-192.
[91]谈杰,李星.网络测量综述[J].计算机应用研究,2006,23(2):5-8.
[92]张宏莉,方滨兴,胡铭曾, et al. Internet测量与分析综述[J].软件学报,2003,14(1):110-116.
[93]田茂泰. P2P网络性能监测研究与实现[D].贵州:贵州大学,2008,67-82.
[94] J. Chen, B. Yuan. Detecting functional modules in the yeast protein–protein interactionnetwork[J]. Bioinformatics,2006,22(18):2283-2290.
[95] S. Fortunato, V. Latora, M. Marchiori. Method to find community structures based oninformation centrality[J]. Physical Review E,2004,70(5):056-104.
[96] S. Gregory. An algorithm to find overlapping community structure in networks[C].Knowledge Discovery in Databases: PKDD2007, Warsaw,2007,91-102.
[97] L. Danon, A. Díaz-Guilera, A. Arenas. The effect of size heterogeneity on communityidentification in complex networks[J]. Journal of Statistical Mechanics: Theory andExperiment,2006,2006(11):110-124.
[98] K. Wakita, T. Tsurumi. Finding community structure in mega-scale socialnetworks:[extended abstract][C]. Proceedings of the16th International Conference on WorldWide Web, Banff,2007,1275-1276.
[99] P. Schuetz, A. Caflisch. Efficient modularity optimization by multistep greedy algorithm andvertex mover refinement[J]. Physical Review E,2008,77(4):046-112.
[100] P. Schuetz, A. Caflisch. Multistep greedy algorithm identifies community structure inreal-world and computer-generated networks[J]. Physical Review E,2008,78(2):026-112.
[101] J. M. Pujol, J. Béjar, J. Delgado. Clustering algorithm for determining community structurein large networks[J]. Physical Review E,2006,74(1):016-107.
[102] H. Du, M. W. Feldman, S. Li, et al. An algorithm for detecting community structure ofsocial networks based on prior knowledge and modularity[J]. Complexity,2007,12(3):53-60.
[103] M. E. J. Newman. Analysis of weighted networks[J]. Physical Review E,2004,70(5):056-131.
[104] L. Donetti, M. A. Munoz. Detecting network communities: a new systematic and efficientalgorithm[J]. Journal of Statistical Mechanics: Theory and Experiment,2004,2004(10):100-122.
[105] J. Duch, A. Arenas. Community detection in complex networks using extremaloptimization[J]. Physical Review E,2005,72(2):027-104.
[106] S. Fortunato. Community detection in graphs[J]. Physics Reports,2010,486(3):75-174.
[107] W. W. Zachary. An information flow model for conflict and fission in small groups[J].Journal of Anthropological Research,1977,4(2):452-473.
[108] H. Liu, X. Qin, H. Yun, et al. A community detecting algorithm in directed weightednetworks[J]. Electrical Engineering and Control,2011,98(3):11-17.
[109] P. Eades, Q.-W. Feng. Multilevel visualization of clustered graphs[C]. Graph Drawing,Rome,1997,101-112.
[110]吴鹏,李思昆.适于社会网络结构分析与可视化的布局算法[J].软件学报,2011,22(10):63-70.
[111] Y. Hu. Algorithms for visualizing large networks[J]. Combinatorial Scientific Computing (toappear),2011,5(3):180-186.
[112] G. Palla, A.-L. Barabási, T. Vicsek. Quantifying social group evolution[J]. Nature,2007,446(7136):664-667.
[113] M. Rosvall, C. T. Bergstrom. Mapping change in large networks[J]. PLoS One,2010,5(1):86-94.
[114]熊站营.基于增量和密度的动态网络社团检测算法[D].西安电子科技大学,2012,20-31.
[115] C. Tantipathananandh, T. Berger-Wolf, D. Kempe. A framework for communityidentification in dynamic social networks[C]. Proceedings of the13th ACM SIGKDDInternational Conference on Knowledge Discovery and Data Mining, San Jose,2007,717-726.
[116] M.-S. Kim, J. Han. A particle-and-density based evolutionary clustering method fordynamic networks[J]. Proceedings of the VLDB Endowment,2009,2(1):622-633.
[117] G. Palla, A.-L. Barabasi, T. Vicsek. Quantifying social group evolution[J]. Nature,2007,446(7136):664-667.
[118] S. Asur, S. Parthasarathy. A viewpoint-based approach for interaction graph analysis[C].Proceedings of the15th ACM SIGKDD International Conference on Knowledge Discoveryand Data Mining, Paris,2009,79-88.
[119] Z. Feng, X. Xu, N. Yuruk, et al. A novel similarity-based modularity function for graphpartitioning[J]. Data Warehousing and Knowledge Discovery,2007,3(2):385-396.
[120] H. W. Kuhn. The hungarian method for the assignment problem[J]. Naval ResearchLogistics Quarterly,2006,2(1-2):83-97.
[121] R. Guimerà, M. Sales-Pardo, L. A. Amaral. Classes of complex networks defined byrole-to-role connectivity profiles[J]. Nature Physics,2006,3(1):63-69.
[122] R. Milo, S. Itzkovitz, N. Kashtan, et al. Superfamilies of evolved and designed networks[J].Science,2004,303(5663):1538-1542.
[123] J. P. Bagrow, E. M. Bollt, J. D. Skufca, et al. Portraits of complex networks[J]. EPL(Europhysics Letters),2008,81(6):68-84.
[124] M. Middendorf, E. Ziv, C. H. Wiggins. Inferring network mechanisms: the drosophilamelanogaster protein interaction network[J]. Proceedings of the National Academy ofSciences of the United States of America,2005,102(9):3192-3197.
[125] E. Estrada. Virtual identification of essential proteins within the protein interaction networkof yeast[J]. Proteomics,2006,6(1):35-40.
[126] D. Gomez, E. González-Arangüena, C. Manuel, et al. Centrality and power in socialnetworks: a game theoretic approach[J]. Mathematical Social Sciences,2003,46(1):27-54.
[127]刘军.社会网络分析导论[M].北京:社会科学文献出版社,2004,19-31.
[128]罗家德.社会网络分析讲义[M].北京:社会科学文献出版社,2005,168-200.
[129] L. C. Freeman. A set of measures of centrality based on betweenness[J]. Sociometry,1977,8(2):35-41.
[130] P. Bonacich. Factoring and weighting approaches to status scores and cliqueidentification[J]. Journal of Mathematical Sociology,1972,2(1):113-120.
[131] P. Bonacich. Power and centrality: a family of measures[J]. American Journal of Sociology,1987,4(6):1170-1182.
[132] R. I. Dunbar. Coevolution of neocortical size, group size and language in humans[J].Behavioral and Brain Sciences,1993,16(4):681-693.
[133] à. Arenas, A. Cabrales, A. Díaz-Guilera, et al. Optimal information transmission inorganizations: search and congestion[J]. Review of Economic Design,2003,14(1-2):75-93.
[134] A. Barrat, M. Barthelemy, R. Pastor-Satorras, et al. The architecture of complex weightednetworks[J]. the National Academy of Sciences of the United States of America,2004,101(11):3747-3752.
[135] M. Latapy. Main-memory triangle computations for very large (sparse (power-law))graphs[J]. Theoretical Computer Science,2008,407(1):458-473.
[136] L. A. N. Amaral, A. Scala, M. Barthélémy, et al. Classes of small-world networks[J].Proceedings of the National Academy of Sciences,2000,97(21):11149-11152.
[137] E. Ravasz, A. L. Barabási. Hierarchical organization in complex networks[J]. PhysicalReview E,2003,67(2):026-112.
[138] M. E. J. Newman. Assortative mixing in networks[J]. Physical Review Letters,2002,89(20):208-701.
[139]张杰. P2P系统中激励相容的机制设计与实现[D].天津:天津大学,2007,24-39.
[140]李江涛.对等网络性能测量与改善[D].北京:北京邮电大学,2006,81-94.
[141]刘维光,陈立伟.一种基于DHT的P2P搜索方法[J].微计算机信息,2006,3(1):131-133.
[142]杨天路. P2P网络技术原理与系统开发案例[M].人民邮电出版社,2007,56-75.
[143]蒋雪玲. P2P流媒体系统多点下载技术的实现研究[J].沈阳大学学报,2006,18(4):63-66.
[144]汪燕,柳斌. BitTorrent协议分析及控制策略[J].实验技术与管理,2006,23(1):54-56.
[145]邓罡.面向网络媒体播放器（IMP）的嵌入式系统的研究与实现[D].长沙:湖南师范大学,2007,16-32.
[146] A. Legout, G. Urvoy-Keller, P. Michiardi. Understanding bittorrent: an experimentalperspective[J]. Research Institute at the Heart of the Information Society,2005,3(2):66-74.
[147] L. Rong, I. Burnett. BitTorrent in a dynamic resource adapting peer-to-peer network[C].First International Conference on Automated Production of Cross Media Content forMulti-Channel Distribution，Washington,2005,69-73.
[148] A. Asvanund, S. Bagla, M. Kapadia, et al. Intelligent club management in peer-to-peernetworks[C]. Proceedings of First Workshop on Economics of P2P, Berkeley,2003,45-52.
[149] J. Li, D. Huang, J. Huang, et al. An extension of HAVE message in BitTorrent systems[C].International Conference on Communication Technology, Tsukuba,2006,1-4.
[150] M. Ripeanu, I. Foster, A. Iamnitchi. Mapping the gnutella network: properties of large-scalepeer-to-peer systems and implications for system design[J]. Distributed, Parallel, andCluster Computing2002,6(8):632-636.
[151] S. Saroiu, P. K. Gummadi, S. D. Gribble. Measurement study of peer-to-peer file sharingsystems[C]. Electronic Imaging2002, San Jose,2002,156-170.
[152] D. Stutzbach, R. Rejaie. Towards a better understanding of churn in peer-to-peernetworks[R]. Oregon: Univ. of Oregon,2004.
[153]刘琼,徐鹏,杨海涛, et al. Peer-to-Peer文件共享系统的测量研究[J].软件学报,2006,17(10):2131-2140.
[154] S. Sen, J. Wang. Analyzing peer-to-peer traffic across large networks[J]. IEEE/ACMTransactions on Networking (ToN),2004,12(2):219-232.
[155] M. Andreolini, R. Lancellotti, P. S. Yu. Analysis of peer-to-peer systems: workloadcharacterization and effects on traffic cacheability[C]. Proceedings of the IEEE ComputerSociety's12th Annual International Symposium on Modeling, Analysis, and Simulation ofComputer and Telecommunications System, Volendam,2004,95-104.
[156] N. Leibowitz, M. Ripeanu, A. Wierzbicki. Deconstructing the kazaa network[C].Proceedings of the Third IEEE Workshop on Internet Applications, San Jose,2003,112-120.
[157] W. Wang, H. Chang, A. Zeitoun, et al. Characterizing guarded hosts in peer-to-peer filesharing systems[C]. Global Telecommunications Conference, Dallas,2004,1539-1543.
[158] D. S. Wallach. A survey of peer-to-peer security issues[J]. Software Security-Theories andSystems,2003,9(3):42-57.
[159] M. Castro, P. Druschel, A. Ganesh, et al. Secure routing for structured peer-to-peer overlaynetworks[J]. ACM SIGOPS Operating Systems Review,2002,36(SI):299-314.
[160] E. Sit, R. Morris. Security considerations for peer-to-peer distributed hash tables[J].Peer-to-Peer Systems,2002,11(2):261-269.
[161] Y. Wang, J. Vassileva. Trust and reputation model in peer-to-peer networks[C]. Proceedingsof third International Conference onPeer-to-Peer Computing, Netherlands,2003,150-157.
[162] S. Bellovin. Security aspects of Napster and Gnutella[C].2001Usenix Annual TechnicalConference, Boston,2001,306-311.
[163] S. Hazel, B. Wiley. Achord: a variant of the chord lookup service for use in censorshipresistant peer-to-peerpublishing systems[C]. Proceedings of the1st International Workshopon Peer-to-Peer Systems (IPTPS’02), Cambridge,2002,34-39.
[164] A. Serjantov. Anonymizing censorship resistant systems[J]. Peer-to-Peer Systems,2002,4(2):111-120.
[165] T. Murphy, T. M. Vii, A. K. Manjhi. Anonymous identity and trust for peer-to-peernetworks[J]. CiteSeer,2002,5(1):20-26.
[166] A. K. Datta, M. Gradinariu, M. Raynal, et al. Anonymous publish/subscribe in p2pnetworks[C]. Proceedings of International Parallel and Distributed Processing Symposium,Nice,2003,8-13.
[167] V. Sadafal. Measurement and analysis of BitTorrent[D]. College Station: Texas A&MUniversity,2008,36-52.
[168] J. R. Douceur. The sybil attack[J]. Peer-to-Peer Systems,2002,8(2):251-260.
[169] D. Xuan, S. Chellappan, X. Wang. Resilience of structured peer to peer systems: Analysisand enhancement[J]. Handbook On Theoretical and Algorithmic Aspects Of Sensor, Ad HocWireless, and Peer-to-Peer Networks,2005,6(2):767-786.
[170] K. Gummadi, R. Gummadi, S. Gribble, et al. The impact of DHT routing geometry onresilience and proximity[C]. Proceedings of the2003Conference on Applications,Technologies, Architectures, and Protocols for Computer Communications, Karlsruhe,2003,381-394.
[171] L. Massoulié, A.-M. Kermarrec, A. J. Ganesh. Network awareness and failure resilience inself-organizing overlay networks[C]. Proceedings of the22nd International Symposium onReliable Distributed Systems, Florence,2003,47-55.
[172] J. Aspnes, Z. Diamadi, G. Shah. Fault-tolerant routing in peer-to-peer systems[C].Proceedings of the twenty-first Annual Symposium on Principles of Distributed Computing,Monterey,2002,223-232.
[173] S. Wang, D. Xuan, W. Zhao. On resilience of structured peer-to-peer systems[C]. GlobalTelecommunications Conference, San Francisco,2003,3851-3856.
[174] D. Loguinov, A. Kumar, V. Rai, et al. Graph-theoretic analysis of structured peer-to-peersystems: routing distances and fault resilience[C]. Proceedings of the2003Conference onApplications, Technologies, Architectures, and Protocols for Computer Communications,Karlsruhe,2003,395-406.
[175] H. Balakrishnan, M. F. Kaashoek, D. Karger, et al. Looking up data in P2P systems[J].Communications of the ACM,2003,46(2):43-48.
[176] B. Gedik, L. Liu. Reliable peer-to-peer information monitoring through replication[C].Proceedings of the22nd International Symposium on Reliable Distributed Systems,Florence,2003,56-65.
[177] J. Risson, T. Moors. Survey of research towards robust peer-to-peer networks: searchmethods[J]. Computer Networks,2006,50(17):3485-3521.
[178] J. Avnet, J. Saia. Towards robust and scalable trust metrics[C].47th Annual IEEESymposium on Foundations of Computer Science, Longdon,2003,196-202.
[179] M. Feldman, K. Lai, I. Stoica, et al. Robust incentive techniques for peer-to-peernetworks[C]. Proceedings of the5th ACM Conference on Electronic Commerce, New York,2004,102-111.
[180] A. Stavrou, D. Rubenstein, S. Sahu. A lightweight, robust P2P system to handle flashcrowds[J]. Selected Areas in Communications, IEEE Journal on,2004,22(1):6-17.
[181] D. Rubenstein, S. Sahu. An analysis of a simple p2p protocol for flash crowd documentretrieval[R]. New York: Columbia University,2001.
[182] M. E. Renda, J. Callan. The robustness of content-based search in hierarchical peer to peernetworks[C]. Proceedings of the thirteenth ACM International Conference on Informationand Knowledge Management, Washington,2004,562-570.
[183] S. Dorogovtsev. Clustering of correlated networks[J]. Phys. Rev. E,2003,69(2):027-104.
[184] S. Zhou, R. J. Mondragn. Accurately modeling the Internet topology[J]. Physical Review E,2004,70(6):066-108.
[185]张国强,张国清. Internet网络的关联性研究[J].软件学报,2006,17(3):490-497.
[186] L. Ramaswamy, B. Gedik, L. Liu. A distributed approach to node clustering in decentralizedpeer-to-peer networks[J]. IEEE Transactions on Parallel and Distributed Systems,2005,5(4):814-829.
[187] F. Le Fessant, S. Handurukande, A. M. Kermarrec, et al. Clustering in peer-to-peer filesharing workloads[J]. Peer-to-Peer Systems III,2005,51(6):217-226.
[188] F. Cantin, B. Gueye, M. A. Kaafar, et al. A self-organized clustering scheme for overlaynetworks[J]. Self-Organizing Systems,2008,7(3):59-70.
[189] J. L. Guillaume, S. Le Blond. Clustering in P2P exchanges[J]. AlgoTel2005SeptiemesRencontres Francopohones Sur Les Aspects Algorithmiques Des Telecommunications,2005,2(4):81-86.
[190] S. B. Handurukande, A. M. Kermarrec, F. L. Fessant, et al. Exploiting semantic clustering inthe edonkey p2p network[C]. Proceedings of the11th Workshop on ACM SIGOPSEuropean Workshop, New York,2004,20-26.
[191] M. Izal, G. Urvoy-Keller, E. W. Biersack, et al. Dissecting bittorrent: five months in atorrent’s lifetime[J]. Passive and Active Network Measurement,2004,11(3):1-11.
[192] B. Krishnamurthy, J. Wang, Y. Xie. Early measurements of a cluster-based architecture forP2P systems[C]. Proceedings of the1st ACM SIGCOMM Workshop on InternetMeasurement, San Francisco,2001,105-109.
[193] E. Adar, B. A. Huberman. Free riding on gnutella[J]. First Monday,2000,5(10-2):56-69.
[194] C. Fraleigh, S. Moon, B. Lyles, et al. Packet-level traffic measurements from the sprint IPbackbone[J]. Network, IEEE,2003,17(6):6-16.
[195] S. Sen, O. Spatscheck, D. Wang. Accurate, scalable in-network identification of p2p trafficusing application signatures[C]. Proceedings of the13th International Conference on WorldWide Web, New York,2004,512-521.
[196] L. A. Adamic, R. M. Lukose, A. R. Puniyani, et al. Search in power-law networks[J].Physical Review E,2001,64(4):046-135.
[197] Q. Lv, P. Cao, E. Cohen, et al. Search and replication in unstructured peer-to-peernetworks[C]. Proceedings of the16th International Conference on Supercomputing, NewYork,2002,84-95.
[198] M. Jovanovi, F. Annexstein, K. Berman. Modeling peer-to-peer network topologiesthrough small-world models and power laws[C]. IX Telecommunications Forum Telfor,Belgrade,2001,1-4.
[199] D. Stutzbach, R. Rejaie, S. Sen. Characterizing unstructured overlay topologies in modernP2P file-sharing systems[J]. Networking, IEEE/ACM Transactions on,2008,16(2):267-280.
[200] D. Stutzbach, R. Rejaie. Capturing accurate snapshots of the Gnutella network[C].Proceedings IEEE INFOCOM2005.24th Annual Joint Conference of the IEEE Computerand Communications Societies, Miami,2005,2825-2830.
[201]郑翔平. BT swarm网络的主动测量与时序性分析[D].成都:电子科技大学,2011,35-47.
[202]李洋. BitTorrent对等网络主动测量研究[D].成都:电子科技大学,2011,80-94.