基于复杂网络的舆情传播模型研究
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摘要
本文从系统结构决定系统功能的角度,利用复杂网络的研究方法对网络舆情的传播规律进行了实证分析和理论研究。随着互联网和信息技术的迅猛发展,网络作为信息发布和传播的载体对舆情和谣言传播的影响越来越大。信息技术的发展为人民生活带来便利的同时,也为谣言和与公共安全相关信息的传播提供了便利的途径。由于网络信息具有动态、交互性等特性,很难对网络信息的传播规律进行定量刻画。复杂网络作为新兴的对复杂系统进行定量描述的工具,可以对复杂自适应系统进行建模和分析。如果把在线社会网络系统中的“个人”和“关系”分别抽象成节点和边,那么可以利用复杂网络理论对在线社会网络的结构对于网络舆情传播的影响进行定量分析和研究。
首先,收集了具有三百五十四万用户的Livejournal在线社会网络数据。将用户定义为节点,用户之间的朋友关系定义为边。实证统计发现Livejournal网站的在线社会网络具有与小世界网络和无标度网络完全不同的结构。该网络由很多星型网络结构组成,而且星型结构的核心节点彼此相连,其入度分布近似满足齐普夫定律,并且稀疏度随着网络规模的扩大而线性增加。利用经典的具有独立更新机制的舆情传播模型,我们进一步研究了该网络对于舆情传播的影响,数值模拟结果发现Livejournal网络中所有用户意见变化之和会很快达到非常小的程度。也就是说Livejournal网络的结构特性非常有利于舆情传播。舆情传播的速度大大超过小世界和无标度网络。进一步,细致研究了全局中心节点和局部中心节点的控制对于网络舆情引导的作用。数值结果发现局部中心节点的控制对于加速或延缓舆情传播的作用并不明显,只是使得系统的演化更加趋于平稳,同时使得系统所有节点的意见趋向于局部中心节点的意见。进一步的模拟发现控制全局中心节点的意见可以大大延缓舆情传播的速度。
其次,对经典舆情传播模型进行了理论解析和数值模拟。引入用户相互影响概率。假设每个用户在与其邻居进行交流的时候,并不完全相信或采纳朋友的观点,而是以一定概率接纳朋友的观点。理论分析和数值模拟发现规则网络中持有不同观点的人群规模比例总会维持在一个稳定水平,而只有对特定的接受概率水平,随机网络中的不同观点才能共存,否则所有的人都会持有相同的意见。进而我们假定模型中存在第三类人群,除了坚持自己观点的两类人群外,还包括可以受邻居影响的人群Z,其中Z类人在与其邻居进行交流的时候,以概率λ接纳朋友的观点。解析分析和数值模拟发现格子网络中持有各种观点的人数比例与初始状态x和Y人群所占的比例有关,而与交流概率无关。而对特定的网络存在特殊的参数也可以使得系统达到平衡状态。
第三,基于链路预测理论的舆情传播模型研究。利用物质扩散理论和热传导理论,构造了准确性和多样性都非常好的链路预测算法。将链路预测算法应用于Livejournal网络中的链路预测。本文收集的Livejournal数据只是全部网络的一个子集。因此,基于链路预测理论的舆情传播模型能够发现更接近实际的传播模式。实证研究发现,加入链路预测后的在线社会网络具有更加快速的传播速度。核心节点之间的沟通作用对于舆情传播的影响非常巨大。进一步的研究发现,降低核心节点的影响力会小幅度加快意见传播的速度。
From the view point of idea that system functions are determined by their structures, this thesis empirically and theoretically study the opinion spreading and guide strategy on complex networks. With the rapid development of information technology and Internet, as a publication and dissemination of the carrier, network is playing more and more significant role in opinion spreading, including the real and groundless informations. Development of information technology has brought convenience to people's lives, and provides a convenient way for the spreading of rumors and information related to public safety. As an effective tool to descript the real-world systems, complex networks have been used quantitatively. If the "personal" and "relationship" in the online social network system are abstracted as nodes and edges, respectively, complex networks can be used for modeling and analysis the opinion spreading process of Web online social networks.
Firstly, an online social network data which has 3.54 million users are statistically analyzed, where the users and relationships are defined as nodes and edges respectively. The statistical results show that this system star-type structure which is totally different from the small-world and scale-free networks, which has joint topology of the core node connected to each other and their in-degree distribution approximately satisfies the Zipfs law and the sparsity is increase linearly with the network size. The classical opinion spreading model with independent update rule is implemented on constructed network and the simulation results show that the change value of all users'opinions will be quick. This results show that opinion spreading speed on real-world system more quickly than the one on small-world and scale-free networks. Furthermore, the effects of the local and global important nodes on the opinion spreading are investigated. The local important nodes could be introduced by their degree. The simulation results indicate that controlling the local important nodes'opinions could not speed up or slow down the opinion evolution process, but the system would evolve more smoothly. In addition, the effects of the global important nodes are considered, the simulation results show that controlling this kind of nodes could slown down the spreading speed greatly. Since much time must be spent to comupute the node betweenness, which has hindered its application.
Secondly, two improved models are studied theoretically and numerically. In the first model, the accepted probability is introduced based on the phenomena that each user would accept his his friends'opinion with probability instead of fully believe or accept. Theoretical analysis and simulation results on grid network show that the proportion of various points of view will always be maintained at a stable level. On a specific probability, different views can coexist on the random networks, otherwise, all the people will hold the same views. In another model, we assume that, beside the persons whose opinions are +1 or -1, there exists the third kind of person, which can be affected by the neighbors and this kind of person would communicate with his neighbors and accept a friend's point of view with a probability. Analytical analysis and simulation results show that the proportion of opinions with +1 or -1 only correlated with the initial proportion of the persons whose opinions are +1 or -1, but has nothing relationship with the exchange probability. The existence of particular network with specific parameters can reach equilibrium.
Thirdly, the opinion spreading model based on the link prediction mechanism is proposed. Based on the mass diffusion and heat conduction processes, a hybrid algorithm is proposed which has higher accuracy and diversity. The improved algorithm is implemented on Livejournal data to study the opinion spreading. Since the livejournal data evolves dynamically and is only a sample of the real data, the opinion spreading model implemented on the network with predicted links would be more practical. Empirical results found that the real opinion would spreading more quickly on the network with predicted links, which indicates that communication between the role of the core nodes would enhance the spreading speed and range greatly. In addition, the simulation results indicate that decreasing the influence of the core nodes could increase the spreading speed correspondingly.
首先,收集了具有三百五十四万用户的Livejournal在线社会网络数据。将用户定义为节点,用户之间的朋友关系定义为边。实证统计发现Livejournal网站的在线社会网络具有与小世界网络和无标度网络完全不同的结构。该网络由很多星型网络结构组成,而且星型结构的核心节点彼此相连,其入度分布近似满足齐普夫定律,并且稀疏度随着网络规模的扩大而线性增加。利用经典的具有独立更新机制的舆情传播模型,我们进一步研究了该网络对于舆情传播的影响,数值模拟结果发现Livejournal网络中所有用户意见变化之和会很快达到非常小的程度。也就是说Livejournal网络的结构特性非常有利于舆情传播。舆情传播的速度大大超过小世界和无标度网络。进一步,细致研究了全局中心节点和局部中心节点的控制对于网络舆情引导的作用。数值结果发现局部中心节点的控制对于加速或延缓舆情传播的作用并不明显,只是使得系统的演化更加趋于平稳,同时使得系统所有节点的意见趋向于局部中心节点的意见。进一步的模拟发现控制全局中心节点的意见可以大大延缓舆情传播的速度。
其次,对经典舆情传播模型进行了理论解析和数值模拟。引入用户相互影响概率。假设每个用户在与其邻居进行交流的时候,并不完全相信或采纳朋友的观点,而是以一定概率接纳朋友的观点。理论分析和数值模拟发现规则网络中持有不同观点的人群规模比例总会维持在一个稳定水平,而只有对特定的接受概率水平,随机网络中的不同观点才能共存,否则所有的人都会持有相同的意见。进而我们假定模型中存在第三类人群,除了坚持自己观点的两类人群外,还包括可以受邻居影响的人群Z,其中Z类人在与其邻居进行交流的时候,以概率λ接纳朋友的观点。解析分析和数值模拟发现格子网络中持有各种观点的人数比例与初始状态x和Y人群所占的比例有关,而与交流概率无关。而对特定的网络存在特殊的参数也可以使得系统达到平衡状态。
第三,基于链路预测理论的舆情传播模型研究。利用物质扩散理论和热传导理论,构造了准确性和多样性都非常好的链路预测算法。将链路预测算法应用于Livejournal网络中的链路预测。本文收集的Livejournal数据只是全部网络的一个子集。因此,基于链路预测理论的舆情传播模型能够发现更接近实际的传播模式。实证研究发现,加入链路预测后的在线社会网络具有更加快速的传播速度。核心节点之间的沟通作用对于舆情传播的影响非常巨大。进一步的研究发现,降低核心节点的影响力会小幅度加快意见传播的速度。
From the view point of idea that system functions are determined by their structures, this thesis empirically and theoretically study the opinion spreading and guide strategy on complex networks. With the rapid development of information technology and Internet, as a publication and dissemination of the carrier, network is playing more and more significant role in opinion spreading, including the real and groundless informations. Development of information technology has brought convenience to people's lives, and provides a convenient way for the spreading of rumors and information related to public safety. As an effective tool to descript the real-world systems, complex networks have been used quantitatively. If the "personal" and "relationship" in the online social network system are abstracted as nodes and edges, respectively, complex networks can be used for modeling and analysis the opinion spreading process of Web online social networks.
Firstly, an online social network data which has 3.54 million users are statistically analyzed, where the users and relationships are defined as nodes and edges respectively. The statistical results show that this system star-type structure which is totally different from the small-world and scale-free networks, which has joint topology of the core node connected to each other and their in-degree distribution approximately satisfies the Zipfs law and the sparsity is increase linearly with the network size. The classical opinion spreading model with independent update rule is implemented on constructed network and the simulation results show that the change value of all users'opinions will be quick. This results show that opinion spreading speed on real-world system more quickly than the one on small-world and scale-free networks. Furthermore, the effects of the local and global important nodes on the opinion spreading are investigated. The local important nodes could be introduced by their degree. The simulation results indicate that controlling the local important nodes'opinions could not speed up or slow down the opinion evolution process, but the system would evolve more smoothly. In addition, the effects of the global important nodes are considered, the simulation results show that controlling this kind of nodes could slown down the spreading speed greatly. Since much time must be spent to comupute the node betweenness, which has hindered its application.
Secondly, two improved models are studied theoretically and numerically. In the first model, the accepted probability is introduced based on the phenomena that each user would accept his his friends'opinion with probability instead of fully believe or accept. Theoretical analysis and simulation results on grid network show that the proportion of various points of view will always be maintained at a stable level. On a specific probability, different views can coexist on the random networks, otherwise, all the people will hold the same views. In another model, we assume that, beside the persons whose opinions are +1 or -1, there exists the third kind of person, which can be affected by the neighbors and this kind of person would communicate with his neighbors and accept a friend's point of view with a probability. Analytical analysis and simulation results show that the proportion of opinions with +1 or -1 only correlated with the initial proportion of the persons whose opinions are +1 or -1, but has nothing relationship with the exchange probability. The existence of particular network with specific parameters can reach equilibrium.
Thirdly, the opinion spreading model based on the link prediction mechanism is proposed. Based on the mass diffusion and heat conduction processes, a hybrid algorithm is proposed which has higher accuracy and diversity. The improved algorithm is implemented on Livejournal data to study the opinion spreading. Since the livejournal data evolves dynamically and is only a sample of the real data, the opinion spreading model implemented on the network with predicted links would be more practical. Empirical results found that the real opinion would spreading more quickly on the network with predicted links, which indicates that communication between the role of the core nodes would enhance the spreading speed and range greatly. In addition, the simulation results indicate that decreasing the influence of the core nodes could increase the spreading speed correspondingly.
引文
[1]郭济.中央和大城市政府应急机制建设[M].北京：中国人民大学出版社,2005.
[2]Dance F E X, Larson C E. The functions of human communication:A theoretical approach [M]. New York:Holt, Rinehart and Winston,1976.
[3]Frank E. X. Dance. The Concept of 'Communication [J]. Journal of Communication,1970, 201-210.
[4]Jurgen R. Technology and. Social Communication [M]. In Communication Theory and Research, edited by L. Thayer. Springfield, Ⅱ:Thomas.1957:462.
[5]Miller G R. On Defining Communication:Another stab [J]. Journal of communication,1966,16: 88-98.
[6]John B. H. English Communication at Colgate Re-Examined [J]. Journal of Communication, 1954,4:76-86.
[7]Berelson B, Gary A S. Human Behavior [M]. New York:Harcourt, Brace,& World,1964.
[8]Stohl C. Organizational Communication:Connectedness in Action [M]. Thousand Oaks, CA: Sage,1995.
[9]Monge P R. The Network Level of Anaysis. In Handbook of Communication Scinece [J], edited by Charles R. Berger and Steven H. Chaffee,239-270. Newbury Park, CA:Sage,1987.
[10]Watts D J, Strogatz S H. Collective dynamics of 'small-world' networks [J]. Nature,1998,393: 440-442.
[11]Barabasi AL, Albert R. Emergence of Scaling in Random Networks [J]. Science,1999,286: 509-512.
[12]Erdos P, Renyi A. On random graphs [J]. Publ. Math.,1959,6:290-297.
[13]Erdos P, Renyi A. On the evolution of random graphs. Publ. Math. Ins. Hung. Acad. Sci.,1960,5: 17-61.
[14]Newman M E J. Assortative mixing in networks [J]. Phys. Rev. Lett.,2002,89:208701.
[15]Ravasz E, Barabasi A L. Hierarchical organization in complex networks [J]. Phys. Rev. E,2003, 67:026112.
[16]Costa L D A F, Rodrigues F A, Travieso G, et al. Characterization of complex networks:A survey of measurements [J]. Adv. Phys.,2007,56:167-242.
[17]Castellano C, Fortunato S, Loreto V. Statistical physics of social dynamics [J]. Reviews of Modern Physics,2009,81:591-646.
[18]Dorogovtsev S N, Mendes J F. Evolution of networks [J]. Adv. Phys.,2002,51:1079-1187.
[19]Albert R, Barabasi A L. Statistical mechanics of complex networks [J]. Rev. Mod. Phys,2002,74: 47-97.
[20]陈关荣.复杂网络及其新近研究进展简介[J].力学进展,2008,25(38)：653-662.
[21]Ramasco J J, Dorogovtsev S N, Pastor-Satorras R. Self-organization of collaboration networks [J]. Phys. Rev. E,2004,70:036106.
[22]Peltomaki M, Alava M. Correlations in bipartite collaboration networks [J]. J. Stat. Mech.: Theory Exp.,2006:P01010.
[23]Ohkubo J, Tanaka K, Horiguchi T. Generation of complex bipartite graphs by using a preferential rewiring process [J]. Phys. Rev. E,2005,72:036120.
[24]Shi D H, Chen Q H, Liu L M. Markov chain-based numerical method for degree distributions of growing networks [J]. Phys. Rev. E,2005,71:036140.
[25]方锦清,毕桥,李永等.复杂动态网络的一种和谐统一的混合择优模型及其普适特性[J].中国科学G辑,2007,37：230-248.
[26]Li X, Chen G, A local-world evolving network model [J]. Physica A,2003,328:274-286.
[27]章忠志.复杂网络的演化模型研究.博士学位论文[辽宁省优秀博士论文],大连理工大学,2006.
[28]Zhou T, Yan G, Wang B H. Maximal planar networks with large clustering coefficient and power-law degree distribution [J]. Phys. Rev. E,2005,71:046141.
[29]李晓佳,张鹏,狄增如等.复杂网络中的社团结构[J].复杂系统与复杂性科学,2008,5(23)：19-42.
[30]刘建国.复杂网络模型构建及其在知识系统中的应用.博士学位论文[大连理工大学优秀博士论文],大连理工大学,2007.
[31]Zhang G Q, Zhang G.Q, Yang Q F, et al. Evolution of the Internet and its cores [J]. New J. Phys., 2008,10:123027.
[32]Stauffer D, Oliveira P M C. Simulation consensus model of never changed opinions in Sznajd consensus model using multi-spin coding [J]. Eprint,2002 arxiv:cond-mat/0208296.
[33]Lambiotte R, Redner S. Dynamics of vacillating voters. J. Stat. Mech.,2007, L10001.
[34]Slanina F, Lavicka H. Analytical results for the Sznajd model of opinion formation [J]. Eur. Phys. J. B,2003,35(2):279-288.
[35]Behera L, Schweitzer F. On Spatial Consensus Formation:Is the Sznajd Model [J]. Int. J. Mod. Phys. C,2003,14(10):1331-1354.
[36]Bernardes A T, Stauffer D, Kertesz J. Election results and the Sznajd model on Barabasi network [J]. Eur.Phys. J. B,2002,25:123-127.
[37]Sznajd-Weron K, Weron R. A simple model of price formation [J]. Int. J. Mod. Phys. C,2002, 13(1):115-123.
[38]Fortunato S. Damage spreading and opinion dynamics on scale-free networks [J]. Physica A, 2005,348:683-690.
[39]Chatterjee S, Seneta E. Towards consencus:some convergence theorems on repeated averaging [J]. J. Appl. Prob.,1977,14:89-97.
[40]Cohen J E, Hajnal J, Newman C M. Approaching consensus can be delicate when positions harden [J]. Stoch. Proc. Appl.,1986,22:315-322.
[41]Stone M. The Opinion Pool [J]. Ann. Math. Stat.,1961,32:1339-1342.
[42]Deffuant G, Neau D, Amblard F, et al. Mixing beliefs among interacting agents [J]. Advance Complex System,2000,3(1-4):87-98.
[43]Hegselmann R, Krause U.Opinion Dynamics Driven by Various Ways of Averaging [J]. Comput. Econ.,2005,25(4):381-405.
[44]Fortunato S. Universality of the threshold for complete consensus for the opinion dynamics of Deffuant et al [J]. Int. J. Mod. Phys. C,2004,15(9):1301-1307.
[45]Lorenz J, Urbig D. About the power to enforce and prevent consensus by manipulating communication rules [J]. Adv. Comp. Syst.,2007,10(2):251-269.
[46]Indekeu J O. Special Attention Network [J]. Physica A,2004,333 (2004) 461-464.
[47]Grabowski A, Kosinski R A. Ising-based model of opinion formation in a complex network of interpersonal interactions [J]. Physica A,2006,361 (2006) 651-664.
[48]Bagnoli F, Berrones A, Franci F. Degustibus. Disputandum (Forecasting Opinions by Knowledge. Networks) [J]. Physica A,2004,332 (2004) 509-518.
[49]Barabasi A L. The origion of bursts and heavy tails of human dynamics [J]. Nature,2005,435: 207-211.
[50]Oliveira J G, Barabasi A L. Darwin and Einstein correspondence patterns [J]. Nature,2005,437: 1251.
[51]Plerou V, Gopikrishnan P, Amaral L A N, et al. Economic fluctuations and anomalous diffusion [J]. Phys. Rev. E,2000,62:3023-3026.
[52]Masoliver J, Montero M, Continuous-time random-walk model for financial distributions [J]. Phys. Rev. E,2003,67:021112.
[53]Politi M, Scalas E. Fitting the empirical distribution of intertrade durations [J]. Physica A,2008, 387:2025-2034.
[54]Jiang Z Q, Chen W, Zhou W X. Scaling in the distribution of intertrade durations of Chinese stocks [J]. Physica A,2008,387:5818-5825.
[55]Dezso Z, Almaas E, Lukacs A, et al. Dynamics of information access on the web [J]. Phys. Rev. E,2006,73:066132.
[56]Goncalves B, Ramasco J J, Human dynamics revealed through Web analytics [J]. Phys. Rev. E, 2008,78:026123.
[57]Zhou T, Kiet H A T, Kim B J, Role of Activity in Human Dynamics [J]. Europhys. Lett.,2008, 82:28002.
[58]Hu H B, Han D Y. Empirical analysis of individual popularity and activity on an online music service system [J]. Physica A,2008,387:5916-5921.
[59]Candia J, Gonzalez M C, Wang P, et al. Uncovering individual and collective human dynamics from mobile phone records [J]. J. Phys. A:Math. Theor.,2008,41:224015.
[60]Henderson T, Nhatti S. Modeling user behavior in networked games [C]. Proc.9th ACM Int. Conf. on Multimetia, ACM Press 2001, pp.212.
[61]Grabowski A, Kruszewska N, Kosinski RA, Dynamic phenomena and human activity in an artificial society [J]. Phys. Rev. E,2008,78:066110.
[62]Baek S K, Kim T Y, Kim B J, Testing a priority-based queue model with Linux command histories [J]. Physica A,387 (2008) 3660-3668.
[63]Goh K I, Barabasi A L, Burstiness and memory in complex systems [J]. Europhys. Lett.,81 (2008)48002.
[64]Liu J G, Wu Z X, Wang F. Opinion Spreading & Consensus Formation on Square Lattice [J]. Int. J. Mod. Phys. C,2007,18:1087-1094.
[65]Wu F, Huberman B A. Social structure and opinion formation [J]. HP Labs Palo Alto, CA 94304, 2006.
[66]Karatzas I, Shreve S E. Brownian motion and stochastic calculus [M],2nd Ed., pp.17, Theorem 3.15, Springer,1997.
[67]聂哲,李粤平,温晓军等.陈健.个体相互影响的网络舆情演变模型[J].计算机工程与应用,2009,45(14)：220-222.
[68]Zhang G Q, Zhang G Q, Yang Q F, et al. Evolution of the Internet and its cores [J]. New Journal of Physics,2008,10:12307.
[69]Brin S, Page L, The anatomy of a large scale hypertextual Web search engine [J]. Comput. Net. ISDN Sys.,1998,30(1-7):107-117.
[70]Kleinberg J M. Authoritative sources in a hyperlinked environment [J]. J. ACM,1999,46(5): 604-632.
[71]Herlocker J L, Konstan J A, Terveen K, et al. Evaluating collaborative filtering recommender systems [J]. ACM Trans. Inform. Syst.,2004,22(1):5-53.
[72]Konstan J A, Miller B N, Maltz D, et al. GroupLens:applying collaborative filtering to Usenet news [J]. Commun. ACM,1997,40(3):77-87.
[73]Liu J G, Wang B H, Guo Q. Improved collaborative filtering algorithm via information transformation [J]. Int. J. Mod. Phys. C,2009,20(02):285-293.
[74]Liu R R, Jia C X, Zhou T, et al. Personal Recommendation via Modified Collaborative Filtering [J]. Physica A,2009,388(4):462-468.
[75]Sun D, Zhou T, Liu J G, et al. Information filtering based on transferring similarity [J]. Phys. Rev. E,2009,80:017101.
[76]Balabanovic M, Shoham Y. Fab:Content-based, collaborative recommendation [J]. Comm. ACM,1997,40(3):66-72.
[77]Pazzani M J. A framework for collaborative, content-based, and demographic filtering [J]. Artif. Intell. Rev.,1999,13(5-6):393-408.
[78]Zhang Y C, Blattner M, Yu Y K. Heat Conduction Process on Community Networks as a Recommendation Model [J]. Phys. Rev. Lett.,2007,99:154301.
[79]Zhang Y C, Medo M, Ren J, et al. Recommendation model based on opinion diffusion [J]. Europhys. Lett.,2008,80:68003.
[80]Zhou T, Ren J, Medo M, et al. Bipartite network projection and personal recommendation [J]. Phys. Rev. E,2005,76:046115.
[81]Zhou T, Jiang L L, Su R Q, et al. Effect of initial configuration on network-based recommendation [J]. Europhys. Lett.,2008,81:58004.
[82]Pazzani M, Billsus D. Learning and Revising User Profiles:The identification of interesting web sites [J]. Machine Learning,1997,27(3):313-331.
[83]Adomavicius G, Tuzhilin A. Toward the next generation of recommender systems-a survey of the state-of-the-art and possible extensions [J]. IEEE Trans. Know.& Data Eng.,2005,17(6): 734-745.
[84]Liu J G, Chen M Z Q, Chen J, et al. Recent advances in personal recommendation systems [J]. Int. J. Info.& Sys. Sci.,2009,5(2):230-247.
[85]刘建国,周涛,汪秉宏.个性化推荐系统的研究进展[J].自然科学进展,2009,15(1)：1-15.
[86]Amaral L A N, Scala A, Barthelemy M, et al. Classes of small-world networks [J]. Proc. Natl. Acad. Sci. U.S.A.,2000,97:11149-11152.
[87]Strogatz S H. Exploring complex networks [J]. Nature,2001,410:268-276.
[88]Newman M E J. The structure and function of complex networks [J]. SIAM Rev.,2003,45: 167-256.
[89]Boccaletti S, Latora V, Moreno Y, et al. Complex networks:Structure and dynamics [J]. Phys. Rep.,2006,424(4-5):175-308.
[90]Holme P, Liljeros F, Edling C R, et al. Network bipartivity [J]. Phys. Rev. E,2003,68:056107.
[91]Liljeros F, Edling C R, Amaral L A N, et al. The Web of Human Sexual Contacts [J]. Nature, 2001,411:907-908.
[92]Jeong H, Tombor B, Albert R, et al. The large-scale organization of metabolic networks [J]. Nature,2000,407:651-654.
[93]Resnick P, Varian H R. Recommender systems [J]. Commun. ACM,1997,40:56-58.
[94]Maslov S, Zhang Y -C. Extracting hidden information from knowledge networks [J]. Phys. Rev. Lett.,2001,87:248701.
[95]Blattner M, Zhang Y-C, Maslov S. Exploring an opinion network for taste prediction:An empirical study [J]. Physica A,2007,373:753-758.
[96]Adoinavicius G, Tuzhilin A. Towards the Next Generation of Recommender Systems:A Survey of the State-of-the-Art and Possible Extensions [J]. IEEE Trans. Know.& Data Eng.,2005,17: 734-749.
[97]Liu J G, Zhou T, Wang B H, et al. Effects of high-order correlations on personalized recommendations for bipartite networks [J]. Physica A,2010,389:881-886.
[98]Liu J G, Zhou T, Wang B H, et al. Degree correlation of bipartite network on personalized recommendation [J]. Int. J. Mod. Phys. C,2010,21(1):137-147.
[99]Zhou T, Kuscsik Z, Liu J -G, et al. Solving the apparent diversity-accuracy dilemma of recommender systems [J]. Proc. Natl. Sci. Acad. U.S.A.,2010,107(10):4511-4515.
[100]Rozenfeld A F, Cohen R, Ben-Avraham D, et al. Scale-Free Networks on Lattices [J]. Phys. Rev. Lett.,2001,89:218701.
[101]Xu X J, Wang W X, Zhou T, et al. Geographical Effects on Epidemic Spreading in Scale-Free Networks [J]. Int. J. Mod. Phys. C,2006,17(12):1815-1822.
[102]Li C, Maini P K. An evolving network model with community structure [J]. J. Phys. A:Math. Gen.,2005,38:9741-9749.
[103]Wang L N, Guo J L, Yang H X, et al. Local preferential attachment model for hierarchical networks [J]. Physica A,2009,388:1713-1720.
[104]Manna S S, Sen P. Modulated scale-free network in Euclidean space [J]. Phys. Rev. E,2002, 66:066114.
[105]Getoor L, Diehl C P. Link Mining:A Survey [J]. ACM SIGKDD Explorations Newsletter, 2005,7:3-12.
[106]Clauset A, Moore C, Newman M E J. Hierarchical structure and the prediction of missing links in networks [J]. Nature,2008,453:98-101.
[107]Redner S. Teasing out the missing links [J]. Nature,2008,453:47-78.
[108]Schafer L, Graham J W. Missing data:Our view of the state of the art [J]. Psychol. Methods, 2002,7:147-177.
[109]Kossinets G. Effects of missing data in social networks [J]. Social Networks,2006,28: 247-268.
[110]Kumar R, Novak J, Tomkins A. Structure and evolution of online social networks [J]. Proc. ACM SIGKDD 2006, ACM Press, New York,2006, p.611.
[111]Guimera R, Sales-Pardo M. Missing and spurious interactions and the reconstruction of complex networks [J]. Proc. Natl. Sci. Acad. U.S.A.,2009,106:22073-22078.
[112]Liu W P, Lu L. Link Prediction Based on Local Random Walk [J]. Europhys. Lett.,2010,89: 58007.
[113]Lu L, Zhou T. Link Prediction in Weighted Networks:The Role of Weak Ties [J]. Europhys. Lett.,2010,89:18001.
[114]Leskovec J, Huttenlocher D, Kleinberg J. Predicting Positive and Negative Links in Online Social Networks [C]. Proc. WWW 2010, ACM, New York,2010.
[115]Antal T, Krapivsky P, Redner S. Dynamics of social balance on networks [J]. Phys. Rev. E, 2005,72:036121.
[116]Marvel S, Strogatz S, Kleinberg J. Energy landscape of social balance [J]. Phys. Rev. Lett., 2009,103:198701.
[117]赵日成.新时期加强高校网络舆情引导的思考[J].中国教育导刊,2009,7：23-24.
[118]曾昭皓,李卫东,林梓坤等.高校共青团网络舆情引导实务探究[J].学理论,2009,31：58-60.
[119]吴小虹.新媒体舆情对领导干部素质的特殊要求[J].求实,2009,12：19-21.
[120]王升华.网络舆情引导策略研究[J].攀登,2009,28(5)：113-115.
[121]王丽平,刘大鹏.开展互联网上舆情控制的方针、对策[J].吉林公安高等专科学校校报,2006，1：109-112.
[122]柯健.公安机关网络舆情预警及对策机制探讨[J].广州市公安管理干部学院学报,2007，4：10-13.
[123]吴绍忠,李淑华.互联网舆情预警机制研究[J].中国人民公安大学学报,2008,3：38-42.
[124]Wasserman S, Faust K. Social network analysis:Methods and applications [M]. New York: Cambridge University Press,1994.
[125]Barnes J A. Graph theory in network analysis [J]. Social Networks,1983,5:235-244.
[126]Freeman C L. Centrality in social networks:Conceptual clarification [J]. Social Networks, 1979,1:215-239.
[127]Freeman C L. A set of measures of centrality based on betweenness [J]. Sociometry,1977,40: 35-41.
[2]Dance F E X, Larson C E. The functions of human communication:A theoretical approach [M]. New York:Holt, Rinehart and Winston,1976.
[3]Frank E. X. Dance. The Concept of 'Communication [J]. Journal of Communication,1970, 201-210.
[4]Jurgen R. Technology and. Social Communication [M]. In Communication Theory and Research, edited by L. Thayer. Springfield, Ⅱ:Thomas.1957:462.
[5]Miller G R. On Defining Communication:Another stab [J]. Journal of communication,1966,16: 88-98.
[6]John B. H. English Communication at Colgate Re-Examined [J]. Journal of Communication, 1954,4:76-86.
[7]Berelson B, Gary A S. Human Behavior [M]. New York:Harcourt, Brace,& World,1964.
[8]Stohl C. Organizational Communication:Connectedness in Action [M]. Thousand Oaks, CA: Sage,1995.
[9]Monge P R. The Network Level of Anaysis. In Handbook of Communication Scinece [J], edited by Charles R. Berger and Steven H. Chaffee,239-270. Newbury Park, CA:Sage,1987.
[10]Watts D J, Strogatz S H. Collective dynamics of 'small-world' networks [J]. Nature,1998,393: 440-442.
[11]Barabasi AL, Albert R. Emergence of Scaling in Random Networks [J]. Science,1999,286: 509-512.
[12]Erdos P, Renyi A. On random graphs [J]. Publ. Math.,1959,6:290-297.
[13]Erdos P, Renyi A. On the evolution of random graphs. Publ. Math. Ins. Hung. Acad. Sci.,1960,5: 17-61.
[14]Newman M E J. Assortative mixing in networks [J]. Phys. Rev. Lett.,2002,89:208701.
[15]Ravasz E, Barabasi A L. Hierarchical organization in complex networks [J]. Phys. Rev. E,2003, 67:026112.
[16]Costa L D A F, Rodrigues F A, Travieso G, et al. Characterization of complex networks:A survey of measurements [J]. Adv. Phys.,2007,56:167-242.
[17]Castellano C, Fortunato S, Loreto V. Statistical physics of social dynamics [J]. Reviews of Modern Physics,2009,81:591-646.
[18]Dorogovtsev S N, Mendes J F. Evolution of networks [J]. Adv. Phys.,2002,51:1079-1187.
[19]Albert R, Barabasi A L. Statistical mechanics of complex networks [J]. Rev. Mod. Phys,2002,74: 47-97.
[20]陈关荣.复杂网络及其新近研究进展简介[J].力学进展,2008,25(38)：653-662.
[21]Ramasco J J, Dorogovtsev S N, Pastor-Satorras R. Self-organization of collaboration networks [J]. Phys. Rev. E,2004,70:036106.
[22]Peltomaki M, Alava M. Correlations in bipartite collaboration networks [J]. J. Stat. Mech.: Theory Exp.,2006:P01010.
[23]Ohkubo J, Tanaka K, Horiguchi T. Generation of complex bipartite graphs by using a preferential rewiring process [J]. Phys. Rev. E,2005,72:036120.
[24]Shi D H, Chen Q H, Liu L M. Markov chain-based numerical method for degree distributions of growing networks [J]. Phys. Rev. E,2005,71:036140.
[25]方锦清,毕桥,李永等.复杂动态网络的一种和谐统一的混合择优模型及其普适特性[J].中国科学G辑,2007,37：230-248.
[26]Li X, Chen G, A local-world evolving network model [J]. Physica A,2003,328:274-286.
[27]章忠志.复杂网络的演化模型研究.博士学位论文[辽宁省优秀博士论文],大连理工大学,2006.
[28]Zhou T, Yan G, Wang B H. Maximal planar networks with large clustering coefficient and power-law degree distribution [J]. Phys. Rev. E,2005,71:046141.
[29]李晓佳,张鹏,狄增如等.复杂网络中的社团结构[J].复杂系统与复杂性科学,2008,5(23)：19-42.
[30]刘建国.复杂网络模型构建及其在知识系统中的应用.博士学位论文[大连理工大学优秀博士论文],大连理工大学,2007.
[31]Zhang G Q, Zhang G.Q, Yang Q F, et al. Evolution of the Internet and its cores [J]. New J. Phys., 2008,10:123027.
[32]Stauffer D, Oliveira P M C. Simulation consensus model of never changed opinions in Sznajd consensus model using multi-spin coding [J]. Eprint,2002 arxiv:cond-mat/0208296.
[33]Lambiotte R, Redner S. Dynamics of vacillating voters. J. Stat. Mech.,2007, L10001.
[34]Slanina F, Lavicka H. Analytical results for the Sznajd model of opinion formation [J]. Eur. Phys. J. B,2003,35(2):279-288.
[35]Behera L, Schweitzer F. On Spatial Consensus Formation:Is the Sznajd Model [J]. Int. J. Mod. Phys. C,2003,14(10):1331-1354.
[36]Bernardes A T, Stauffer D, Kertesz J. Election results and the Sznajd model on Barabasi network [J]. Eur.Phys. J. B,2002,25:123-127.
[37]Sznajd-Weron K, Weron R. A simple model of price formation [J]. Int. J. Mod. Phys. C,2002, 13(1):115-123.
[38]Fortunato S. Damage spreading and opinion dynamics on scale-free networks [J]. Physica A, 2005,348:683-690.
[39]Chatterjee S, Seneta E. Towards consencus:some convergence theorems on repeated averaging [J]. J. Appl. Prob.,1977,14:89-97.
[40]Cohen J E, Hajnal J, Newman C M. Approaching consensus can be delicate when positions harden [J]. Stoch. Proc. Appl.,1986,22:315-322.
[41]Stone M. The Opinion Pool [J]. Ann. Math. Stat.,1961,32:1339-1342.
[42]Deffuant G, Neau D, Amblard F, et al. Mixing beliefs among interacting agents [J]. Advance Complex System,2000,3(1-4):87-98.
[43]Hegselmann R, Krause U.Opinion Dynamics Driven by Various Ways of Averaging [J]. Comput. Econ.,2005,25(4):381-405.
[44]Fortunato S. Universality of the threshold for complete consensus for the opinion dynamics of Deffuant et al [J]. Int. J. Mod. Phys. C,2004,15(9):1301-1307.
[45]Lorenz J, Urbig D. About the power to enforce and prevent consensus by manipulating communication rules [J]. Adv. Comp. Syst.,2007,10(2):251-269.
[46]Indekeu J O. Special Attention Network [J]. Physica A,2004,333 (2004) 461-464.
[47]Grabowski A, Kosinski R A. Ising-based model of opinion formation in a complex network of interpersonal interactions [J]. Physica A,2006,361 (2006) 651-664.
[48]Bagnoli F, Berrones A, Franci F. Degustibus. Disputandum (Forecasting Opinions by Knowledge. Networks) [J]. Physica A,2004,332 (2004) 509-518.
[49]Barabasi A L. The origion of bursts and heavy tails of human dynamics [J]. Nature,2005,435: 207-211.
[50]Oliveira J G, Barabasi A L. Darwin and Einstein correspondence patterns [J]. Nature,2005,437: 1251.
[51]Plerou V, Gopikrishnan P, Amaral L A N, et al. Economic fluctuations and anomalous diffusion [J]. Phys. Rev. E,2000,62:3023-3026.
[52]Masoliver J, Montero M, Continuous-time random-walk model for financial distributions [J]. Phys. Rev. E,2003,67:021112.
[53]Politi M, Scalas E. Fitting the empirical distribution of intertrade durations [J]. Physica A,2008, 387:2025-2034.
[54]Jiang Z Q, Chen W, Zhou W X. Scaling in the distribution of intertrade durations of Chinese stocks [J]. Physica A,2008,387:5818-5825.
[55]Dezso Z, Almaas E, Lukacs A, et al. Dynamics of information access on the web [J]. Phys. Rev. E,2006,73:066132.
[56]Goncalves B, Ramasco J J, Human dynamics revealed through Web analytics [J]. Phys. Rev. E, 2008,78:026123.
[57]Zhou T, Kiet H A T, Kim B J, Role of Activity in Human Dynamics [J]. Europhys. Lett.,2008, 82:28002.
[58]Hu H B, Han D Y. Empirical analysis of individual popularity and activity on an online music service system [J]. Physica A,2008,387:5916-5921.
[59]Candia J, Gonzalez M C, Wang P, et al. Uncovering individual and collective human dynamics from mobile phone records [J]. J. Phys. A:Math. Theor.,2008,41:224015.
[60]Henderson T, Nhatti S. Modeling user behavior in networked games [C]. Proc.9th ACM Int. Conf. on Multimetia, ACM Press 2001, pp.212.
[61]Grabowski A, Kruszewska N, Kosinski RA, Dynamic phenomena and human activity in an artificial society [J]. Phys. Rev. E,2008,78:066110.
[62]Baek S K, Kim T Y, Kim B J, Testing a priority-based queue model with Linux command histories [J]. Physica A,387 (2008) 3660-3668.
[63]Goh K I, Barabasi A L, Burstiness and memory in complex systems [J]. Europhys. Lett.,81 (2008)48002.
[64]Liu J G, Wu Z X, Wang F. Opinion Spreading & Consensus Formation on Square Lattice [J]. Int. J. Mod. Phys. C,2007,18:1087-1094.
[65]Wu F, Huberman B A. Social structure and opinion formation [J]. HP Labs Palo Alto, CA 94304, 2006.
[66]Karatzas I, Shreve S E. Brownian motion and stochastic calculus [M],2nd Ed., pp.17, Theorem 3.15, Springer,1997.
[67]聂哲,李粤平,温晓军等.陈健.个体相互影响的网络舆情演变模型[J].计算机工程与应用,2009,45(14)：220-222.
[68]Zhang G Q, Zhang G Q, Yang Q F, et al. Evolution of the Internet and its cores [J]. New Journal of Physics,2008,10:12307.
[69]Brin S, Page L, The anatomy of a large scale hypertextual Web search engine [J]. Comput. Net. ISDN Sys.,1998,30(1-7):107-117.
[70]Kleinberg J M. Authoritative sources in a hyperlinked environment [J]. J. ACM,1999,46(5): 604-632.
[71]Herlocker J L, Konstan J A, Terveen K, et al. Evaluating collaborative filtering recommender systems [J]. ACM Trans. Inform. Syst.,2004,22(1):5-53.
[72]Konstan J A, Miller B N, Maltz D, et al. GroupLens:applying collaborative filtering to Usenet news [J]. Commun. ACM,1997,40(3):77-87.
[73]Liu J G, Wang B H, Guo Q. Improved collaborative filtering algorithm via information transformation [J]. Int. J. Mod. Phys. C,2009,20(02):285-293.
[74]Liu R R, Jia C X, Zhou T, et al. Personal Recommendation via Modified Collaborative Filtering [J]. Physica A,2009,388(4):462-468.
[75]Sun D, Zhou T, Liu J G, et al. Information filtering based on transferring similarity [J]. Phys. Rev. E,2009,80:017101.
[76]Balabanovic M, Shoham Y. Fab:Content-based, collaborative recommendation [J]. Comm. ACM,1997,40(3):66-72.
[77]Pazzani M J. A framework for collaborative, content-based, and demographic filtering [J]. Artif. Intell. Rev.,1999,13(5-6):393-408.
[78]Zhang Y C, Blattner M, Yu Y K. Heat Conduction Process on Community Networks as a Recommendation Model [J]. Phys. Rev. Lett.,2007,99:154301.
[79]Zhang Y C, Medo M, Ren J, et al. Recommendation model based on opinion diffusion [J]. Europhys. Lett.,2008,80:68003.
[80]Zhou T, Ren J, Medo M, et al. Bipartite network projection and personal recommendation [J]. Phys. Rev. E,2005,76:046115.
[81]Zhou T, Jiang L L, Su R Q, et al. Effect of initial configuration on network-based recommendation [J]. Europhys. Lett.,2008,81:58004.
[82]Pazzani M, Billsus D. Learning and Revising User Profiles:The identification of interesting web sites [J]. Machine Learning,1997,27(3):313-331.
[83]Adomavicius G, Tuzhilin A. Toward the next generation of recommender systems-a survey of the state-of-the-art and possible extensions [J]. IEEE Trans. Know.& Data Eng.,2005,17(6): 734-745.
[84]Liu J G, Chen M Z Q, Chen J, et al. Recent advances in personal recommendation systems [J]. Int. J. Info.& Sys. Sci.,2009,5(2):230-247.
[85]刘建国,周涛,汪秉宏.个性化推荐系统的研究进展[J].自然科学进展,2009,15(1)：1-15.
[86]Amaral L A N, Scala A, Barthelemy M, et al. Classes of small-world networks [J]. Proc. Natl. Acad. Sci. U.S.A.,2000,97:11149-11152.
[87]Strogatz S H. Exploring complex networks [J]. Nature,2001,410:268-276.
[88]Newman M E J. The structure and function of complex networks [J]. SIAM Rev.,2003,45: 167-256.
[89]Boccaletti S, Latora V, Moreno Y, et al. Complex networks:Structure and dynamics [J]. Phys. Rep.,2006,424(4-5):175-308.
[90]Holme P, Liljeros F, Edling C R, et al. Network bipartivity [J]. Phys. Rev. E,2003,68:056107.
[91]Liljeros F, Edling C R, Amaral L A N, et al. The Web of Human Sexual Contacts [J]. Nature, 2001,411:907-908.
[92]Jeong H, Tombor B, Albert R, et al. The large-scale organization of metabolic networks [J]. Nature,2000,407:651-654.
[93]Resnick P, Varian H R. Recommender systems [J]. Commun. ACM,1997,40:56-58.
[94]Maslov S, Zhang Y -C. Extracting hidden information from knowledge networks [J]. Phys. Rev. Lett.,2001,87:248701.
[95]Blattner M, Zhang Y-C, Maslov S. Exploring an opinion network for taste prediction:An empirical study [J]. Physica A,2007,373:753-758.
[96]Adoinavicius G, Tuzhilin A. Towards the Next Generation of Recommender Systems:A Survey of the State-of-the-Art and Possible Extensions [J]. IEEE Trans. Know.& Data Eng.,2005,17: 734-749.
[97]Liu J G, Zhou T, Wang B H, et al. Effects of high-order correlations on personalized recommendations for bipartite networks [J]. Physica A,2010,389:881-886.
[98]Liu J G, Zhou T, Wang B H, et al. Degree correlation of bipartite network on personalized recommendation [J]. Int. J. Mod. Phys. C,2010,21(1):137-147.
[99]Zhou T, Kuscsik Z, Liu J -G, et al. Solving the apparent diversity-accuracy dilemma of recommender systems [J]. Proc. Natl. Sci. Acad. U.S.A.,2010,107(10):4511-4515.
[100]Rozenfeld A F, Cohen R, Ben-Avraham D, et al. Scale-Free Networks on Lattices [J]. Phys. Rev. Lett.,2001,89:218701.
[101]Xu X J, Wang W X, Zhou T, et al. Geographical Effects on Epidemic Spreading in Scale-Free Networks [J]. Int. J. Mod. Phys. C,2006,17(12):1815-1822.
[102]Li C, Maini P K. An evolving network model with community structure [J]. J. Phys. A:Math. Gen.,2005,38:9741-9749.
[103]Wang L N, Guo J L, Yang H X, et al. Local preferential attachment model for hierarchical networks [J]. Physica A,2009,388:1713-1720.
[104]Manna S S, Sen P. Modulated scale-free network in Euclidean space [J]. Phys. Rev. E,2002, 66:066114.
[105]Getoor L, Diehl C P. Link Mining:A Survey [J]. ACM SIGKDD Explorations Newsletter, 2005,7:3-12.
[106]Clauset A, Moore C, Newman M E J. Hierarchical structure and the prediction of missing links in networks [J]. Nature,2008,453:98-101.
[107]Redner S. Teasing out the missing links [J]. Nature,2008,453:47-78.
[108]Schafer L, Graham J W. Missing data:Our view of the state of the art [J]. Psychol. Methods, 2002,7:147-177.
[109]Kossinets G. Effects of missing data in social networks [J]. Social Networks,2006,28: 247-268.
[110]Kumar R, Novak J, Tomkins A. Structure and evolution of online social networks [J]. Proc. ACM SIGKDD 2006, ACM Press, New York,2006, p.611.
[111]Guimera R, Sales-Pardo M. Missing and spurious interactions and the reconstruction of complex networks [J]. Proc. Natl. Sci. Acad. U.S.A.,2009,106:22073-22078.
[112]Liu W P, Lu L. Link Prediction Based on Local Random Walk [J]. Europhys. Lett.,2010,89: 58007.
[113]Lu L, Zhou T. Link Prediction in Weighted Networks:The Role of Weak Ties [J]. Europhys. Lett.,2010,89:18001.
[114]Leskovec J, Huttenlocher D, Kleinberg J. Predicting Positive and Negative Links in Online Social Networks [C]. Proc. WWW 2010, ACM, New York,2010.
[115]Antal T, Krapivsky P, Redner S. Dynamics of social balance on networks [J]. Phys. Rev. E, 2005,72:036121.
[116]Marvel S, Strogatz S, Kleinberg J. Energy landscape of social balance [J]. Phys. Rev. Lett., 2009,103:198701.
[117]赵日成.新时期加强高校网络舆情引导的思考[J].中国教育导刊,2009,7：23-24.
[118]曾昭皓,李卫东,林梓坤等.高校共青团网络舆情引导实务探究[J].学理论,2009,31：58-60.
[119]吴小虹.新媒体舆情对领导干部素质的特殊要求[J].求实,2009,12：19-21.
[120]王升华.网络舆情引导策略研究[J].攀登,2009,28(5)：113-115.
[121]王丽平,刘大鹏.开展互联网上舆情控制的方针、对策[J].吉林公安高等专科学校校报,2006，1：109-112.
[122]柯健.公安机关网络舆情预警及对策机制探讨[J].广州市公安管理干部学院学报,2007，4：10-13.
[123]吴绍忠,李淑华.互联网舆情预警机制研究[J].中国人民公安大学学报,2008,3：38-42.
[124]Wasserman S, Faust K. Social network analysis:Methods and applications [M]. New York: Cambridge University Press,1994.
[125]Barnes J A. Graph theory in network analysis [J]. Social Networks,1983,5:235-244.
[126]Freeman C L. Centrality in social networks:Conceptual clarification [J]. Social Networks, 1979,1:215-239.
[127]Freeman C L. A set of measures of centrality based on betweenness [J]. Sociometry,1977,40: 35-41.
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