R软件的知识结构与开发者合作结构及其演化研究
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摘要
在没有市场机制引导的大众生产领域也能实现知识的分工与合作已经是一个不争的事实，但这种新兴生产方式下的产品的知识结构及演化机制却未被充分认识。本文以著名的开源软件——R软件为对象，研究其知识结构和开发者合作结构及其演化机制，以期达到认识开源软件乃至大众生产过程中知识聚集过程的目的。主要工作及结论如下：
首先研究体现R软件知识结构的软件包网络，发现随着时间的变化，网络规模呈幂律增长趋势，稠密度随规模增加而下降，网络凝聚性随规模增加而减弱，表明R软件中的知识呈加速凝聚的态势，知识结构呈现扩张趋势；同时，R软件的发展方式表现为最大连通片主体不断吸收聚集游离小连通片；在知识结构的扩展中，R软件保持了功能主轴不分叉的特性。对R软件的知识结构建立树关系网络模型分析，发现其存在核心结构，并成功识别出知识结构的功能社团。
进一步分析与知识结构形成紧密相关的开发者合作结构，发现它不但具有自身的演化特点，而且与R软件的知识结构存在耦合现象。这一发现揭示了从合作-技术双维度的新视角对开源软件进行分类研究的意义。通过对合作结构最大连通图的深入分析，发现合作结构有两种基本模式，一是以开发者为中心，二是以软件包为中心。
随后基于随机行动者导向模型，本文研究了R软件知识结构和开发者合作结构的内生演化机制以及两种结构的共演机制，发现了统计上显著的网络微观结构是驱动两个网络演化以及共演的内生机制。实证研究表明R软件包网络的演化中根节点及其子节点共同衍生新节点的机制在统计上是显著正面作用的；合作结构二模网演化中合作延续性机制是有显著的正面作用的；而软件包之间的技术联系对合作网络中的软件包共开发者关系形成有显著的正面促进作用。这些发现拓宽了对于网络演化机制的认识。
本文的主要贡献体现在：在知识分工新视角下，以R软件为代表，运用多种网络分析方法，尤其是动态演化的网络分析方法，研究大众生产知识聚集过程中的知识结构以及与之紧密相关的合作结构。在研究方法上，本文提出了基于无圈有向网络的树关系网络建模方法、能够同时识别核心与社团结构的Hub-社团分析方法，推广了随机行动者导向模型，提出了合作-技术这个物理-事理的分析框架。在研究内容上，本文深入剖析了R软件的知识结构、开发者合作结构，以及它们的内生演化机制。研究结论对认识大众生产模式下的知识产品生产过程具有理论价值，同时对软件开发过程的知识管理具有实践参考价值。
It is an indisputable fact that the knowledge division and cooperation can realizein the production fields of peer production. But the knowledge accumulation processunder this new mode of production has not been fully understand this new productionmode of. In this paper, the machanism and order behind OSS is explored from theperspect of the knowledge structure and the developer cooperation structure and itsevolution based on the famous R software as the research object. Main works andconclusions are as follows:
First the study of the knowledge structure of R software shows: the networkscale submit to power law distirbution over time, as the scale growing, the density andthe cohesion decreases,which means the accelerated cohesive trend of the knowledgeof R software and the expansive trend of R software knowledge structure; R softwaredeveloped through that the maximal connected component constantly absorb andgather free and small connective components.As the expansion of knowledgestructure, the spindle of R software knowledge structure does not fork. Based on thetree relationship network model analysis, we found the existence of the core structurein the R software and successfully identify the functional communities in theknowledge structure.
There exist a close relationship between knowledge structure and cooperativestructure of R software. The study shows there exist evolution properties in thecooperational structure and the study also found the relationship couple phenomenabetween co-developer packages networks and R package network, reflecting therelationship overlap at the dimensions of people and technolgy that inspiring a"technology-cooperation" perspective to explore the properties of OSS.
Employing the stochastic actor oriented model(SAOM), the paper studied on theevolution machanism of knowledge structure and developers cooperational structureof R software. And the results reveals that there are some statistically significantendogenous network structure which drive these two structure evolution. Besidespreferential attachemnt which also know as indegree popularity, such as transitivity effect, four-cycles, out-indegree assortativity are statistical significant also. Thesefindings broaden the knowledge of the mechanisms for network evolution.
The main contribution of this paper is the research of knowledge structure andcooperative structure of R software at a new perspective on the division of knowledge,with method of dynamic evolution complex network analysis. At the methodsperspective, this paper presents tree modeling method based on acyclic directednetwork and no-hub-community detection method which can indentify communityfrom the network with core and community both; generalizes SAOM to moreapplication fields; proposes a "technology-cooperation" framework to analyse OSS.In conclusion, the research is helpful theoretically to understand production process ofpeer production and useful to the knowledge management of software development.
首先研究体现R软件知识结构的软件包网络，发现随着时间的变化，网络规模呈幂律增长趋势，稠密度随规模增加而下降，网络凝聚性随规模增加而减弱，表明R软件中的知识呈加速凝聚的态势，知识结构呈现扩张趋势；同时，R软件的发展方式表现为最大连通片主体不断吸收聚集游离小连通片；在知识结构的扩展中，R软件保持了功能主轴不分叉的特性。对R软件的知识结构建立树关系网络模型分析，发现其存在核心结构，并成功识别出知识结构的功能社团。
进一步分析与知识结构形成紧密相关的开发者合作结构，发现它不但具有自身的演化特点，而且与R软件的知识结构存在耦合现象。这一发现揭示了从合作-技术双维度的新视角对开源软件进行分类研究的意义。通过对合作结构最大连通图的深入分析，发现合作结构有两种基本模式，一是以开发者为中心，二是以软件包为中心。
随后基于随机行动者导向模型，本文研究了R软件知识结构和开发者合作结构的内生演化机制以及两种结构的共演机制，发现了统计上显著的网络微观结构是驱动两个网络演化以及共演的内生机制。实证研究表明R软件包网络的演化中根节点及其子节点共同衍生新节点的机制在统计上是显著正面作用的；合作结构二模网演化中合作延续性机制是有显著的正面作用的；而软件包之间的技术联系对合作网络中的软件包共开发者关系形成有显著的正面促进作用。这些发现拓宽了对于网络演化机制的认识。
本文的主要贡献体现在：在知识分工新视角下，以R软件为代表，运用多种网络分析方法，尤其是动态演化的网络分析方法，研究大众生产知识聚集过程中的知识结构以及与之紧密相关的合作结构。在研究方法上，本文提出了基于无圈有向网络的树关系网络建模方法、能够同时识别核心与社团结构的Hub-社团分析方法，推广了随机行动者导向模型，提出了合作-技术这个物理-事理的分析框架。在研究内容上，本文深入剖析了R软件的知识结构、开发者合作结构，以及它们的内生演化机制。研究结论对认识大众生产模式下的知识产品生产过程具有理论价值，同时对软件开发过程的知识管理具有实践参考价值。
It is an indisputable fact that the knowledge division and cooperation can realizein the production fields of peer production. But the knowledge accumulation processunder this new mode of production has not been fully understand this new productionmode of. In this paper, the machanism and order behind OSS is explored from theperspect of the knowledge structure and the developer cooperation structure and itsevolution based on the famous R software as the research object. Main works andconclusions are as follows:
First the study of the knowledge structure of R software shows: the networkscale submit to power law distirbution over time, as the scale growing, the density andthe cohesion decreases,which means the accelerated cohesive trend of the knowledgeof R software and the expansive trend of R software knowledge structure; R softwaredeveloped through that the maximal connected component constantly absorb andgather free and small connective components.As the expansion of knowledgestructure, the spindle of R software knowledge structure does not fork. Based on thetree relationship network model analysis, we found the existence of the core structurein the R software and successfully identify the functional communities in theknowledge structure.
There exist a close relationship between knowledge structure and cooperativestructure of R software. The study shows there exist evolution properties in thecooperational structure and the study also found the relationship couple phenomenabetween co-developer packages networks and R package network, reflecting therelationship overlap at the dimensions of people and technolgy that inspiring a"technology-cooperation" perspective to explore the properties of OSS.
Employing the stochastic actor oriented model(SAOM), the paper studied on theevolution machanism of knowledge structure and developers cooperational structureof R software. And the results reveals that there are some statistically significantendogenous network structure which drive these two structure evolution. Besidespreferential attachemnt which also know as indegree popularity, such as transitivity effect, four-cycles, out-indegree assortativity are statistical significant also. Thesefindings broaden the knowledge of the mechanisms for network evolution.
The main contribution of this paper is the research of knowledge structure andcooperative structure of R software at a new perspective on the division of knowledge,with method of dynamic evolution complex network analysis. At the methodsperspective, this paper presents tree modeling method based on acyclic directednetwork and no-hub-community detection method which can indentify communityfrom the network with core and community both; generalizes SAOM to moreapplication fields; proposes a "technology-cooperation" framework to analyse OSS.In conclusion, the research is helpful theoretically to understand production process ofpeer production and useful to the knowledge management of software development.
引文
[1] Airoldi, Edoardo, Blei, David, Xing, Eric, et al. A latent mixed membership model forrelational data; proceedings of the Proceedings of the3rd international workshop on Linkdiscovery, F,2005[C]. ACM.
[2] Albert, Reka, Jeong, Hawoong, Barabasi, Albert-Laszlo. Internet: Diameter of the World-WideWeb [J]. Nature,1999,401(6749):130-131.
[3] Albert, Reka, Jeong, Hawoong, Barabasi, Albert-Laszlo. Error and attack tolerance ofcomplex networks [J]. Nature,2000,406(6794):378-382.
[4] Anderson, C. J., Wasserman, S., Crouch, B. A p*primer: Logit models for social networks [J].Social Networks,1999,21(1):37–66.
[5] Backstrom, Lars, Huttenlocher, Dan, Kleinberg, Jon, et al. Group formation in large socialnetworks: membership, growth, and evolution; proceedings of the Proceedings of the12thACM SIGKDD international conference on Knowledge discovery and data mining, F,2006
[C]. ACM.
[6] Barabási, Albert-László. Scale-free networks: a decade and beyond [J]. Science,2009,325(5939):412-413.
[7] Barabási, Albert-László, Albert, Réka. Emergence of scaling in random networks [J]. science,1999,286(5439):509-512.
[8] Barabási, Albert-László, Oltvai, Zoltan N. Network biology: understanding the cell'sfunctional organization [J]. Nature Reviews Genetics,2004,5(2):101-113.
[9] Barabasi, Albert-Laszlo, Jeong, Hawoong, Néda, Zoltan, et al. Evolution of the social networkof scientific collaborations [J]. Physica A: Statistical Mechanics and its Applications,2002,311(3):590-614.
[10] Barcellini, Flore, Détienne, Fran oise, Burkhardt, Jean-Marie. Participation in onlineinteraction spaces: Design-use mediation in an Open Source Software community [J].International Journal of Industrial Ergonomics,2009,39(3):533-540.
[11] Basset, Thomas. Coordination and social structures in an open source project: Videolan [J].Open Source Software Development,2004,125-151.
[12] Batagelj, Vladimir, Bren, Matevz. Comparing resemblance measures [J]. Journal ofclassification,1995,12(1):73-90.
[13] Bearman, Peter S, Moody, James, Stovel, Katherine. Chains of Affection: The Structure ofAdolescent Romantic and Sexual Networks1[J]. American Journal of Sociology,2004,110(1):44-91.
[14] Becker, Gary S, Murphy, Kevin M. The Division of Labor, Coordination Costs, andKnowledge [J]. The Quarterly Journal of Economics,1992,107(4):1137-1160.
[15] Benabou, R., Tirole, J. Intrinsic and extrinsic motivation [J]. Review of Economic Studies,2003,70(3):489–520.
[16] Bender, E. A., Canfield, E. R. The asymptotic number of labeled graphs with given degreesequences [J]. Journal of Combinatorial Theory, Series A,1978,24(3):296–307.
[17] Benkler, Y. Coase's Penguin, or, Linux and the Nature of the Firm [J]. Yale Law Journal,2002,112(3):367–445.
[18] Benkler, Y. The wealth of networks: How social production transforms markets and freedom
[M]. Yale University Press,2006.
[19] Benkler, Y., Nissenbaum, H. Commons-based peer production and virtue [J]. Journal ofPolitical Philosophy,2006,14(4):394.
[20] Berge, Claude, Minieka, Edward. Graphs and hypergraphs [M]. North-Holland publishingcompany Amsterdam,1973.
[21] Besag, Julian. Spatial Interaction and the Statistical Analysis of Lattice Systems [J]. Journal ofthe Royal Statistical Society. Series B (Methodological),1974,36(2):192-236.
[22] Besag, Julian. Statistical Analysis of Non-Lattice Data [J]. Journal of the Royal StatisticalSociety. Series D (The Statistician),1975,24(3):179-195.
[23] Bessen, James. Open source software: Free provision of complex public goods [J]. Availableat SSRN588763,2005.
[24] Bishop, Yvonne M, Fienberg, Stephen E, Holland, Paul W. Discrete multivariate analysis:theory and practice [M]. Springer,2007.
[25] BlackDuck. Black Duck Software Estimates Development Cost of Open Source Software at$387Billion. from http://www.blackducksoftware.com/news/releases/2009-04-14
[26] Blondel, Vincent D, Guillaume, Jean-Loup, Lambiotte, Renaud, et al. Fast unfolding ofcommunities in large networks [J]. Journal of Statistical Mechanics: Theory and Experiment,2008,10): P10008.
[27] Bollobás, Béla, Riordan, Oliver, Spencer, Joel, et al. The degree sequence of a scale‐freerandom graph process [J]. Random Structures&Algorithms,2001,18(3):279-290.
[28] Bonaccorsi, A., Rossi, C. Comparing motivations of individual programmers and firms to takepart in the open source movement: From community to business [J]. Knowledge, Technology&Policy,2006,18(4):40–64.
[29] Borgatti, Stephen P.2-mode concepts in social network analysis [J]. Encyclopedia ofComplexity and System Science,2009,8279-8291.
[30] Borgatti, Stephen P., Everett, Martin G. Network analysis of2-mode data [J]. Social Networks,1997,19(3):243-269.
[31] Broder, Andrei, Kumar, Ravi, Maghoul, Farzin, et al. Graph structure in the web [J]. Computernetworks,2000,33(1):309-320.
[32] Carley, Kathleen M, Skillicorn, David. Special issue on analyzing large scale networks: TheEnron corpus [J]. Computational&Mathematical Organization Theory,2005,11(3):179-181.
[33] Chambers, John M. Facets of R [J]. The R Journal,2009,1(1):5-8.
[34] Chen, P, Redner, S. Community structure of the physical review citation network [J]. Journalof Informetrics,2010,4(3):278-290.
[35] Christakis, Nicholas A, Fowler, James H. The spread of obesity in a large social network over32years [J]. New England journal of medicine,2007,357(4):370-379.
[36] Clauset, Aaron, Moore, Cristopher, Newman, Mark EJ. Hierarchical structure and theprediction of missing links in networks [J]. Nature,2008,453(7191):98-101.
[37] Clauset, Aaron, Shalizi, Cosma Rohilla, Newman, M. E. J. Power-law distributions inempirical data [J]. SIAM review,2009,51(4):661-703.
[38] CMU, Department of Statistics. from http://www.stat.cmu.edu/
[39] Cohendet, Patrick, Creplet, Frederic, Dupouet, Olivier. Organisational innovation,communities of practice and epistemic communities: the case of Linux [M]. Economics withHeterogeneous Interacting Agents. Springer.2001:303-326.
[40] Croissant, Yves, Millo, Giovanni. Panel data econometrics in R: The plm package [J]. Journalof Statistical Software,2008,27(2):1-43.
[41] Crowston, K., Howison, J. The social structure of free and open source software development[J]. First Monday,2005,10(2-7).
[42] Crowston, Kevin, Howison, James. Hierarchy and centralization in free and open sourcesoftware team communications [J]. Knowledge, Technology&Policy,2006,18(4):65-85.
[43] Desmarais, Bruce A, Cranmer, Skyler J. Statistical inference for valued-edge networks: thegeneralized exponential random graph model [J]. PloS one,2012,7(1): e30136.
[44] Dorogovtsev, S. N., Goltsev, A. V., Mendes, J. F. F. Critical phenomena in complex networks[J]. Reviews of Modern Physics,2008,80(4):1275–1335.
[45] Durrett, Richard. Random graph dynamics [M]. Cambridge university press,2007.
[46] Eom Y-H, Fortunato S. Characterizing and Modeling Citation Dynamics [J]. PLoS ONE,2011,
[47] érdi, P, Makovi, Kinga, Somogyvári, Zoltán, et al. Prediction of emerging technologies basedon analysis of the US patent citation network [J]. Scientometrics,2013,95(1):225-242.
[48] Erdos, Paul, Rényi, A. On the evolution of random graphs [J]. Publ. Math. Inst. Hungar. Acad.Sci,1960,5:17-61.
[49] Faloutsos, Michalis, Faloutsos, Petros, Faloutsos, Christos. On power-law relationships of theinternet topology; proceedings of the ACM SIGCOMM Computer Communication Review, F,1999[C]. ACM.
[50] Fang, Yulin, Neufeld, Derrick. Understanding sustained participation in open source softwareprojects [J]. Journal of Management Information Systems,2009,25(4):9-50.
[51] Fortunato, S. Community detection in graphs [J]. Physics Reports,2010,486(3-5):75–174.
[52] Foster, J. G., Foster, D. V., Grassberger, P., et al. Edge direction and the structure of networks[J]. Proceedings of the National Academy of Sciences,2010,107(24):10815.
[53] Fox, John. Aspects of the Social Organization and Trajectory of the R Project [J]. The RJournal,2009,1(2):5-13.
[54] Frank, Ove, Strauss, David. Markov graphs [J]. Journal of the american Statistical association,1986,81(395):832-842.
[55] Gale, D. A theorem onflows in networks [J]. Pacific J. Math,1957,7:1073–1082.
[56] Gao, Yongqin, Freeh, Vince, Madey, Greg. Analysis and modeling of the open source softwarecommunity [J]. NAACSOS, Pittsburgh,2003,
[57] Ghosh, Rishab Aiyer. Interviews with Linus Torvalds: What motivates software developers [J].First Monday,1998,3(2).
[58] Ghosh, Rishab Aiyer. Economic impact of open source software on innovation and thecompetitiveness of the Information and Communication Technologies (ICT) sector in the EU[J].2007,
[59] Girvan, Michelle, Newman, M. E. J. Community structure in social and biological networks[J]. Proceedings of the National Academy of Sciences,2002,99(12):7821-7826.
[60] Godfrey, Michael W, Tu, Qiang. Evolution in open source software: A case study; proceedingsof the Software Maintenance,2000. Proceedings. International Conference on, F,2000[C].IEEE.
[61] Goldenberg, Anna, Moore, Andrew. Tractable learning of large Bayes net structures fromsparse data; proceedings of the Proceedings of the twenty-first international conference onMachine learning, F,2004[C]. ACM.
[62] Gonzalez-Barahona, Jesus M, Robles, Gregorio, Andradas-Izquierdo, Roberto, et al.Geographic origin of libre software developers [J]. Information Economics and Policy,2008,20(4):356-363.
[63] Guillaume, Jean-Loup, Latapy, Matthieu. Bipartite structure of all complex networks [J].Information processing letters,2004,90(5):215-221.
[64] Guillaume, Jean-Loup, Latapy, Matthieu. Bipartite graphs as models of complex networks [J].Physica A: Statistical Mechanics and its Applications,2006,371(2):795-813.
[65] Hammersley, J. M., Clifford, P. Markov field on finite graphs and lattices [J]. Unpublished,1971,
[66] Handcock, Mark S, Robins, Garry, Snijders, T. A. B., et al. Assessing degeneracy in statisticalmodels of social networks [M]. Working paper.2003.
[67] Hann, I. H., Roberts, J., Slaughter, S., et al. Economic incentives for participating in opensource software projects [M]. Proceedings of the Twenty-Third International Conference onInformation Systems.2002:365–372.
[68] Hann, Il-Horn, Roberts, Jeff, Slaughter, Sandra. Why developers participate in open sourcesoftware projects: An empirical investigation [J].2004,
[69] Hanneke, Steve, Fu, Wenjie, Xing, Eric P. Discrete temporal models of social networks [J].Electronic Journal of Statistics,2010,4:585-605.
[70] Hars, Alexander, Ou, Shaosong. Working for free? Motivations of participating in open sourceprojects; proceedings of the System Sciences,2001. Proceedings of the34th Annual HawaiiInternational Conference on, F,2001[C]. IEEE.
[71] Harvard, University. statistics courses. from http://www.registrar.fas.harvard.edu/statistics
[72] Hauck Jr, W. W., Donner, A. Wald's test as applied to hypotheses in logit analysis [J]. Journalof the American Statistical Association,1977,72):851-853.
[73] Hayek, Friedrich August. The Use of Knowledge in Soceity [J]. The American EconomicReview,1945,35(4):519-530.
[74] Haythornthwaite, Caroline. Crowds and communities: Light and heavyweight models of peerproduction; proceedings of the System Sciences,2009. HICSS'09.42nd Hawaii InternationalConference on, F,2009[C]. IEEE.
[75] Henkel, Joachim. Selective revealing in open innovation processes: The case of embeddedLinux [J]. Research policy,2006,35(7):953-969.
[76] Holland, John M. Hidden order: How adaptation builds complexity[M]. Basic Books,1995.
[77] Holland, Paul W., Leinhardt, Samuel. An Exponential Family of Probability Distributions forDirected Graphs [J]. Journal of the American Statistical Association,1981,76(373):33.
[78] Holme, Petter, Kim, Beom Jun, Yoon, Chang No, et al. Attack vulnerability of complexnetworks [J]. Physical Review E,2002,65(5):056109.
[79] Hu, Xiaojun, Rousseau, Ronald, Chen, Jin. A new approach for measuring the value of patentsbased on structural indicators for ego patent citation networks [J]. Journal of the AmericanSociety for Information Science and Technology,2012,63(9):1834-1842.
[80] Hunt, Francis, Johnson, Paul. On the Pareto distribution of Sourceforge projects; proceedingsof the Proceedings of the Open Source Software Development Workshop, F,2002[C].
[81] Ihaka, Ross. A Brief History R: Past and Future History. fromhttp://cran.r-project.org/doc/html/interface98-paper/paper_2.html
[82] JoPP. Mission Statement.2012, from http://peerproduction.net/about/mission-statement/
[83] Katz, Leo. The distribution of the number of isolates in a social group [J]. The Annals ofMathematical Statistics,1952,23(2):271-276.
[84] KDnuggets. What Analytics, Data mining, Big Data software you used in the past12monthsfor a real project?, fromhttp://www.kdnuggets.com/polls/2012/analytics-data-mining-big-data-software.html
[85] Kitsak, Maksim, Gallos, Lazaros K, Havlin, Shlomo, et al. Identification of influentialspreaders in complex networks [J]. Nature Physics,2010,6(11):888-893.
[86] Kleinberg, Jon. The small-world phenomenon: an algorithm perspective; proceedings of theProceedings of the thirty-second annual ACM symposium on Theory of computing, F,2000
[C]. ACM.
[87] Kleinberg, Jon. Small-world phenomena and the dynamics of information [J]. Advances inneural information processing systems,2002,1:431-438.
[88] Kleinberg, Jon M. Navigation in a small world [J]. Nature,2000,406(6798):845-845.
[89] Koch, Stefan. Free/open source software development [M]. Igi Global,2005.
[90] Koskinen, Johan, Edling, Christofer. Modelling the evolution of a bipartite network—Peerreferral in interlocking directorates [J]. Social Networks,2012,34(3):309-322.
[91] Lakhani, K. R., Von Hippel, E. How open source software works [J]. Research policy,2003,32(6):923–943.
[92] Lancashire, David. Code, culture and cash: The fading altruism of open source development[J]. First Monday,2001,6(12):
[93] Lancichinetti, A., Radicchi, F., Ramasco, J. J. Finding statistically significant communities innetworks [J]. PloS one,2011,6(4): e18961.
[94] Lancichinetti, Andrea, Radicchi, Filippo, Ramasco, José J. Statistical significance ofcommunities in networks [J]. Physical Review E,2010,81(4):046110.
[95] Lerner, J., Pathak, P. A., Tirole, J. The dynamics of open-source contributors [J]. TheAmerican economic review,2006,96(2):114–118.
[96] Lerner, J., Tirole, J. The open source movement: Key research questions [J]. EuropeanEconomic Review,2001,45(4-6):819–826.
[97] Lerner, J., Tirole, J. Some simple economics of open source [J]. The journal of industrialeconomics,2002,50(2):197–234.
[98] Lerner, J., Tirole, J. The economics of technology sharing: open source and beyond [J].Journal of Economic Perspectives,2005,19(2):99–120.
[99] Leskovec, Jure, Kleinberg, Jon, Faloutsos, Christos. Graph evolution: Densification andshrinking diameters [J]. ACM Transactions on Knowledge Discovery from Data (TKDD),2007,1(1):2.
[100] Leuf, Bo, Cunningham, Ward. The Wiki way: quick collaboration on the Web [J].2001,
[101] Liben‐Nowell, David, Kleinberg, Jon. The link‐prediction problem for social networks [J].Journal of the American society for information science and technology,2007,58(7):1019-1031.
[102] Lipnack, Jessica. Virtual teams: Reaching across space, time, and organizations withtechnology [M]. Jeffrey Stamps,1997.
[103] Madey, G., Freeh, V., Tynan, R. The open source software development phenomenon: Ananalysis based on social network theory [M]. Americas Conference on Information Systems(AMCIS2002).2002.
[104] Mahadevan, P., Krioukov, D., Fall, K., et al. Systematic topology analysis and generationusing degree correlations [J]. ACM SIGCOMM Computer Communication Review,2006,36(4):135–146.
[105] Maslov, S., Sneppen, K. Specificity and stability in topology of protein networks [J]. Science,2002,296(5569):910.
[106] Maslov, S., Sneppen, K., Zaliznyak, A. Detection of topological patterns in complex networks:correlation profile of the internet [J]. Physica A: Statistical and Theoretical Physics,2004,333(529–540.
[107] McCallum, Andrew, Corrada-Emmanuel, Andres, Wang, Xuerui. Topic and role discovery insocial networks [J]. Computer Science Department Faculty Publication Series,2005,3.
[108] Mcfadden, D. Conditional Logit Analysis of Qualitative Choice Behavior [J]. Frontiers inEconometrics,1974,105-142.
[109] McKee, Terry A, McMorris, Fred R. Topics in intersection graph theory [M]. Siam,1999.
[110] McPherson, Amanda, Proffitt, Brian, Hale-Evans, Ron. Estimating the Total DevelopmentCost of a Linux Distribution [J]. The Linux Foundation,2008,
[111] Mercken, Liesbeth, Snijders, T. A. B., Steglich, Christian, et al. Dynamics of adolescentfriendship networks and smoking behavior: Social network analyses in six European countries[J]. Social science&medicine,2009,69(10):1506-1514.
[112] Milgram, Stanley. The small world problem [J]. Psychology today,1967,2(1):60-67.
[113] Milo, R., Itzkovitz, S., Kashtan, N., et al. Superfamilies of evolved and designed networks [J].Science,2004,303(5663):1538.
[114] Milo, R., Shen-Orr, S., Itzkovitz, S., et al. Network motifs: simple building blocks of complexnetworks [J]. Science,2002,298(5594):824.
[115] Mockus, Audris, Fielding, Roy T, Herbsleb, James. A case study of open source softwaredevelopment: the Apache server; proceedings of the Software Engineering,2000. Proceedingsof the2000International Conference on, F,2000[C]. IEEE.
[116] Moreno, Jacob Levy. Who shall survive?: A new approach to the problem of humaninterrelations [J].1934,
[117] Moreno, Yamir, Pastor-Satorras, Romualdo, Vespignani, Alessandro. Epidemic outbreaks incomplex heterogeneous networks [J]. The European Physical Journal B-Condensed Matterand Complex Systems,2002,26(4):521-529.
[118] Mostafavi, Sara, Ray, Debajyoti, Warde-Farley, David, et al. GeneMANIA: a real-timemultiple association network integration algorithm for predicting gene function [J]. GenomeBiol,2008,9(Suppl1): S4.
[119] Nagurney, Anna. On the relationship between supply chain and transportation networkequilibria: A supernetwork equivalence with computations [J]. Transportation Research Part E:Logistics and Transportation Review,2006,42(4):293-316.
[120] Nagurney, Anna, Cruz, Jose, Dong, June, et al. Supply chain networks, electronic commerce,and supply side and demand side risk [J]. European Journal of Operational Research,2005,164(1):120-142.
[121] Nagurney, Anna, Cruz, Jose, Matsypura, Dmytro. Dynamics of global supply chainsupernetworks [J]. Mathematical and Computer Modelling,2003,37(9):963-983.
[122] Nagurney, Anna, Dong, June. Supernetworks: decision-making for the information age [M].Elgar, Edward Publishing, Incorporated,2002.
[123] Nagurney, Anna, Toyasaki, Fuminori. Supply chain supernetworks and environmental criteria[J]. Transportation Research Part D: Transport and Environment,2003,8(3):185-213.
[124] Nakakoji, Kumiyo, Yamamoto, Yasuhiro. The co-evolution of systems and communities infree and open source software development [J]. Free/open source software development,2005,59.
[125] Newman, M. E. J. Scientific collaboration networks. I. Network construction and fundamentalresults [J]. Physical review E,2001a,64(1):016131.
[126] Newman, M. E. J. The structure of scientific collaboration networks [J]. Proceedings of theNational Academy of Sciences,2001b,98(2):404-409.
[127] Newman, M. E. J. Modularity and community structure in networks [J]. Proceedings of theNational Academy of Sciences,2006,103(23):8577-8582.
[128] Newman, M. E. J. Networks: an introduction [M]. Oxford University Press,2009.
[129] Newman, M. E. J. Assortative mixing in networks [J]. Physical Review Letters,2002,89(20):208701.
[130] Newman, M. E. J. The Structure and Function of Complex Networks [J]. SIAM Review,2003,45(2):167-256.
[131] Newman, M. E. J., Girvan, M. Finding and evaluating community structure in networks [J].Physical review E,2004,69(2):026113.
[132] Newman, M. E. J., Strogatz, S. H., Watts, D. J. Random graphs with arbitrary degreedistributions and their applications [J]. Physical Review E,2001,64(2):026118.
[133] Newman, M. E. J., Watts, D. J., Strogatz, S. H. Random graph models of social networks [J].Proceedings of the National Academy of Sciences of the United States of America,2002,99(Suppl1):2566.
[134] Nyman, Linus, Mikkonen, Tommi. To Fork or Not to Fork: Fork Motivations in SourceForgeProjects [M]. IGI Global.2011:1-9.
[135] O'Reilly, T. Lessons from open source software development [J]. Communications of theACM,1999,42(4):33-37.
[136] O’Madadhain, Joshua, Smyth, Padhraic, Adamic, Lada. Learning predictive models for linkformation; proceedings of the International Sunbelt Social Network Conference, F,2005[C].
[137] Opsahl, Tore. Triadic closure in two-mode networks: Redefining the global and localclustering coefficients [J]. Social Networks,2013,35(2):159-167.
[138] Oracle. fromhttp://www.oracle.com/technetwork/cn/database/options/advanced-analytics/r-enterprise/index.html
[139] Orman, Wafa Hakim. Giving it away for free? The nature of job-market signaling byopen-source software developers [J]. The BE Journal of Economic Analysis&Policy,2008,8(1):
[140] Ou, Ruiqiu., Yang, Jianmei. On structural properties of scale-free networks with finite size [J].Physica a-Statistical Mechanics and Its Applications,2012,391(3):887-894.
[141] Papadopoulos, Fragkiskos, Kitsak, Maksim, Serrano, M ángeles, et al. Popularity versussimilarity in growing networks [J]. Nature,2012,489(7417):537-540.
[142] Park, J., Newman, M. E. J. Statistical mechanics of networks [J]. Physical Review E,2004,70(6):066117.
[143] Pastor-Satorras, Romualdo, Vespignani, Alessandro. Complex networks: Patterns ofcomplexity [J]. Nat Phys,2010,6(7):480-481.
[144] Pattison, Philippa, Wasserman, Stanley. Logit models and logistic regressions for socialnetworks: II. Multivariate relations [J]. British Journal of Mathematical and StatisticalPsychology,1999,52(2):169-193.
[145] Pittel, Boris. On tree census and the giant component in sparse random graphs [J]. RandomStructures&Algorithms,1990,1(3):311-342.
[146] Raymond, E. The cathedral and the bazaar [J]. Knowledge, Technology&Policy,1999,12(3):23–49.
[147] RevolutionAnalytics. from http://www.revolutionanalytics.com/
[148] Rinaldo, Alessandro, Fienberg, Stephen E, Zhou, Yi. On the geometry of discrete exponentialfamilies with application to exponential random graph models [J]. Electronic Journal ofStatistics,2009,3(446-484).
[149] Ripley, Ruth, Boitmanis, Krists, Snijders, T.A.B.(2013). RSiena: Siena-SimulationInvestigation for Empirical Network Analysis (Version1.1-232). Retrieved fromhttp://CRAN.R-project.org/package=RSiena
[150] Robillard, Pierre N. The role of knowledge in software development [J]. Commun. ACM,1999,42(1):87-92.
[151] Robins, Garry, Alexander, Malcolm. Small worlds among interlocking directors: Networkstructure and distance in bipartite graphs [J]. Computational&Mathematical OrganizationTheory,2004,10(1):69-94.
[152] Robins, Garry, Pattison, Philippa, Wasserman, Stanley. Logit models and logistic regressionsfor social networks: III. Valued relations [J]. Psychometrika,1999,64(3):371-394.
[153] Robins, Garry, Pattison, Pip, Kalish, Yuval, et al. An introduction to exponential random graph(p*) models for social networks [J]. Social Networks,2007,29(2):173-191.
[154] Sampson, Samuel Franklin. A novitiate in a period of change: An experimental and case studyof social relationships [D]; Cornell University, September,1968.
[155] Sarkar, Purnamrita, Moore, Andrew W. Dynamic social network analysis using latent spacemodels [J]. ACM SIGKDD Explorations Newsletter,2005,7(2):31-40.
[156] Sarkar, Purnamrita, Siddiqi, Sajid M, Gordon, Geoffrey J. A latent space approach to dynamicembedding of co-occurrence data; proceedings of the International Conference on ArtificialIntelligence and Statistics, F,2007[C].
[157] Schweinberger, Michael. Statistical modelling of network panel data: Goodness of fit [J].British Journal of Mathematical and Statistical Psychology,2012,65(2):263-281.
[158] Seidman, Stephen B. Network structure and minimum degree [J]. Social networks,1983,5(3):269-287.
[159] Shah, Sonali K. Motivation, governance, and the viability of hybrid forms in open sourcesoftware development [J]. Management Science,2006,52(7):1000-1014.
[160] Simmel, George. The sociology of georg simmel [M]. SimonandSchuster. com,1950.
[161] Simon, Herbert A. On a class of skew distribution functions [J]. Biometrika,1955,42(3/4):425-440.
[162] Snijders, T. A. B. Stochastic actor-oriented models for network change [J]. Journal ofmathematical sociology,1996,21(1-2):149–172.
[163] Snijders, T. A. B. The statistical evaluation of social network dynamics [J]. Sociologicalmethodology,2001,31(1):361–395.
[164] Snijders, T. A. B. Statistical models for social networks [J]. Annual Review of Sociology,2011,37:131-153.
[165] Snijders, T. A. B., Van de Bunt, G. G., Steglich, C. E. G. Introduction to stochastic actor-basedmodels for network dynamics [J]. Social Networks,2010,32(1):44–60.
[166] Stallman, Richard M, Gay, Joshua. Free software, free society: Selected essays of Richard M.Stallman [M]. CreateSpace,2009.
[167] Steglich, C., Snijders, T. A. B., Pearson, M. Dynamic networks and behavior: Separatingselection from influence [J]. Sociological Methodology,2010,
[168] Steglich, Christian. Longitudinal network analysis involving special data types [M]. WorkshopSocial Network Analysis2011. University of Groningen.2011.
[169] Stol, Klaas-Jan, Babar, Muhammad Ali. Reporting empirical research in open source software:the state of practice [M]. Open Source Ecosystems: Diverse Communities Interacting.Springer.2009:156-169.
[170] Stouffer, Daniel B, Malmgren, R Dean, Amaral, Luís AN. Log-normal statistics in e-mailcommunication patterns [J]. arXiv preprint physics/0605027,2006,
[171] Stouffer, Daniel B, Malmgren, R Dean, Amaral, Lu s AN. Comment on Barabási [J]. Nature,2005,435(207).
[172] Strauss, David, Ikeda, Michael. Pseudolikelihood estimation for social networks [J]. Journalof the American Statistical Association,1990,85(409):204-212.
[173] Subramanyam, Ramanath, Xia, Mu. Free/Libre Open Source Software development indeveloping and developed countries: A conceptual framework with an exploratory study [J].Decision support systems,2008,46(1):173-186.
[174] TIOBE. TIOBE Programming Community Index for May2013. fromhttp://www.tiobe.com/index.php/content/paperinfo/tpci/index.html
[175] Torvalds, Linus. The linux edge [J]. Communications of the ACM,1999,42(4):38-39.
[176] Travers, Jeffrey, Milgram, Stanley. An experimental study of the small world problem [J].Sociometry,1969,425-443.
[177] Van De Bunt, Gerhard G, Van Duijn, Marijtje AJ, Snijders, T. A. B. Friendship networksthrough time: An actor-oriented dynamic statistical network model [J]. Computational&Mathematical Organization Theory,1999,5(2):167-192.
[178] Verspagen, Bart. Mapping technological trajectories as patent citation networks: a study on thehistory of fuel cell research [J]. Advances in Complex Systems,2007,10(01):93-115.
[179] Von Hippel, Eric. The sources of innovation [M]. Springer,2007.
[180] Wagstrom, Patrick Adam, Herbsleb, Jim, Carley, Kathleen. A social network approach tofree/open source software simulation; proceedings of the Proceedings First InternationalConference on Open Source Systems, F,2005[C].
[181] Wang, Jing, Hu, Michael Y, Shanker, Murali. Human agency, social networks, and FOSSproject success [J]. Journal of Business Research,2012,65(7):977-984.
[182] Wasserman, S., Pattison, P. Logit models and logistic regressions for social networks: I. Anintroduction to Markov graphs andp [J]. Psychometrika,1996,61(3):401–425.
[183] Wasserman, Stanley. Social network analysis: Methods and applications [M]. Cambridgeuniversity press,1994.
[184] Watts, D. J., Strogatz, S. H. Collective dynamics of ‘small-world’networks [J]. Nature,1998,393(6684):440–442.
[185] Wellman, Barry. An electronic group is virtually a social network [J]. Culture of the Internet,1997,4:179-205.
[186] West, Joel, O'Mahony, Siobhán. The Role of Participation Architecture in Growing SponsoredOpen Source Communities [J]. Industry and Innovation,2008,15(2):145-168.
[187] West, Joel, O'Mahony, Siobhan. Contrasting community building in sponsored and communityfounded open source projects; proceedings of the System Sciences,2005. HICSS'05.Proceedings of the38th Annual Hawaii International Conference on, F,2005[C]. IEEE.
[188] Wu, Chorng-Guang, Gerlach, James H, Young, Clifford E. An empirical analysis of opensource software developers’ motivations and continuance intentions [J]. Information&Management,2007,44(3):253-262.
[189] Xu, Bo, Jones, Donald R, Shao, Bingjia. Volunteers’ involvement in online community basedsoftware development [J]. Information&management,2009,46(3):151-158.
[190] Xu, Jin. Mining and modeling the open source software community [D]; University of NotreDame,2008.
[191] Xu, Jin., Christley, S., Madey, G. Application of social network analysis to the study of opensource software [J]. The economics of open source software development,2006,205–224.
[192] Yang, Jianmei, Lu, Lvping, Xie, Wangdan, et al. On competitive relationship networks: A newmethod for industrial competition analysis [J]. Physica A: Statistical Mechanics and itsApplications,2007,382(2):704-714.
[193] Yang, Jianmei, Zhuang, Dong, Xu, Xiao. The complex network analysis on service channelsof a bank and its management utility [J]. Dynamics of Continuous Discrete and ImpulsiveSystems Series B,2008,15(2):179.
[194] Yang, Xiaokai, Borland, Jeff. A Microeconomic Mechanism for Economic Growth [J]. Journalof Political Economy,1991,460-482.
[195] Ye, Yunwen, Kishida, Kouichi. Toward an understanding of the motivation of open sourcesoftware developers; proceedings of the Software Engineering,2003. Proceedings.25thInternational Conference on, F,2003[C]. IEEE.
[196] Zhang, Pei-Pei, Chen, Kan, He, Yue, et al. Model and empirical study on some collaborationnetworks [J]. Physica A: Statistical Mechanics and its applications,2006,360(2):599-616.
[197]阿尔温·托夫勒.第三次浪潮[M].北京:生活.读书.新知三联书店,2006.
[198]阿林杨格.报酬递增与经济进步[J].经济社会体制比较,1996,3):52-57.
[199]保罗·萨缪尔森,诺德豪斯.经济学（第17版）[M].北京:人民邮电出版社,2004.
[200]毕桥,方锦清.网络科学与统计物理方法[M].北京:北京大学出版社,2011.
[201]曾进群,杨建梅,陈泉.基于加权网络模型的开源软件社区大众生产结构研究[J].工业工程,2013,6):
[202]曾进群,杨建梅,陈泉, et al.基于零模型的开源社区大众生产合作网络结构研究[J].华南理工大学学报(社会科学版),2013,4):
[203]常静.百度百科及开源社区参与者的动机与行为关系的实证研究[D];华南理工大学,2009.
[204]陈关荣,汪小帆,李翔. Introduction to Complex Networks: Models, Structures andDynamics [M].北京:高等教育出版社,2012.
[205]陈泉,杨建梅,曾进群.零模型及其在复杂网络研究中的应用[J].复杂系统与复杂性科学,2013,10(1):8-17.
[206]方锦清,汪小帆,郑志刚, et al.一门崭新的交叉科学:网络科学(上)[J].物理学进展,2007,27(3):239-343.
[207]方锦清,汪小帆,郑志刚, et al.一门崭新的交叉科学:网络科学(下篇)[J].物理学进展,2007,27(004):361–448.
[208]哈耶克.致命的自负[M].北京:中国社会科学出版社,2000.
[209]哈耶克.个人主义与经济秩序[M].北京:生活.读书.新知三联书店,2003.
[210]杰夫·豪.众包:大众力量缘何推动商业未来[M].北京:中信出版社,2009.
[211]卡尔波普尔.论三个世界[M].通过知识获得解放.北京;中国美术学院出版社.1998.
[212]克里斯·安德森.长尾理论[M].北京:中信出版社,2006.
[213]李欣荣.百度百科与谷歌开源社区比较研究[D];华南理工大学,2011.
[214]罗宾斯.管理学[M].北京:人民大学出版社,2004.
[215]马歇尔.经济学原理[M].北京:商务印书馆,1965.
[216]欧瑞秋.网络博弈视角下的中国汽车企业对抗互动研究[D],2010.
[217]潘向东.开源社区稳定性研究[D].广州;华南理工大学,2009.
[218]史定华.网络度分布理论[M].北京:高等教育出版社,2011.
[219]唐·泰普斯科特,安东尼·D·威廉姆斯.维基经济学:大规模协作如何改变一切[M].北京:中国青年出版社,2007.
[220]汪小帆,李翔,陈关荣.网络科学导论[M].北京:高等教育出版社,2012.
[221]王志平,王众托.超网络理论及其应用[M].科学出版社,2008.
[222]谢益辉,郑冰.(2008). R语言的历史背景、发展历程和现状. Paper presented at the1stChina R Conference,北京.
[223]亚当斯密.国民财富的性质和原因的研究[M].北京:商务印书馆,1988.
[224]杨建梅.复杂网络与社会网络研究范式的比较[J].系统工程理论与实践,2010,11.
[225]于洋.组织知识管理中的知识超网络研究[D];大连理工大学,2009.
[226]周连强. Android开源操作系统的大众生产研究[D].广州;华南理工大学,2011.
[2] Albert, Reka, Jeong, Hawoong, Barabasi, Albert-Laszlo. Internet: Diameter of the World-WideWeb [J]. Nature,1999,401(6749):130-131.
[3] Albert, Reka, Jeong, Hawoong, Barabasi, Albert-Laszlo. Error and attack tolerance ofcomplex networks [J]. Nature,2000,406(6794):378-382.
[4] Anderson, C. J., Wasserman, S., Crouch, B. A p*primer: Logit models for social networks [J].Social Networks,1999,21(1):37–66.
[5] Backstrom, Lars, Huttenlocher, Dan, Kleinberg, Jon, et al. Group formation in large socialnetworks: membership, growth, and evolution; proceedings of the Proceedings of the12thACM SIGKDD international conference on Knowledge discovery and data mining, F,2006
[C]. ACM.
[6] Barabási, Albert-László. Scale-free networks: a decade and beyond [J]. Science,2009,325(5939):412-413.
[7] Barabási, Albert-László, Albert, Réka. Emergence of scaling in random networks [J]. science,1999,286(5439):509-512.
[8] Barabási, Albert-László, Oltvai, Zoltan N. Network biology: understanding the cell'sfunctional organization [J]. Nature Reviews Genetics,2004,5(2):101-113.
[9] Barabasi, Albert-Laszlo, Jeong, Hawoong, Néda, Zoltan, et al. Evolution of the social networkof scientific collaborations [J]. Physica A: Statistical Mechanics and its Applications,2002,311(3):590-614.
[10] Barcellini, Flore, Détienne, Fran oise, Burkhardt, Jean-Marie. Participation in onlineinteraction spaces: Design-use mediation in an Open Source Software community [J].International Journal of Industrial Ergonomics,2009,39(3):533-540.
[11] Basset, Thomas. Coordination and social structures in an open source project: Videolan [J].Open Source Software Development,2004,125-151.
[12] Batagelj, Vladimir, Bren, Matevz. Comparing resemblance measures [J]. Journal ofclassification,1995,12(1):73-90.
[13] Bearman, Peter S, Moody, James, Stovel, Katherine. Chains of Affection: The Structure ofAdolescent Romantic and Sexual Networks1[J]. American Journal of Sociology,2004,110(1):44-91.
[14] Becker, Gary S, Murphy, Kevin M. The Division of Labor, Coordination Costs, andKnowledge [J]. The Quarterly Journal of Economics,1992,107(4):1137-1160.
[15] Benabou, R., Tirole, J. Intrinsic and extrinsic motivation [J]. Review of Economic Studies,2003,70(3):489–520.
[16] Bender, E. A., Canfield, E. R. The asymptotic number of labeled graphs with given degreesequences [J]. Journal of Combinatorial Theory, Series A,1978,24(3):296–307.
[17] Benkler, Y. Coase's Penguin, or, Linux and the Nature of the Firm [J]. Yale Law Journal,2002,112(3):367–445.
[18] Benkler, Y. The wealth of networks: How social production transforms markets and freedom
[M]. Yale University Press,2006.
[19] Benkler, Y., Nissenbaum, H. Commons-based peer production and virtue [J]. Journal ofPolitical Philosophy,2006,14(4):394.
[20] Berge, Claude, Minieka, Edward. Graphs and hypergraphs [M]. North-Holland publishingcompany Amsterdam,1973.
[21] Besag, Julian. Spatial Interaction and the Statistical Analysis of Lattice Systems [J]. Journal ofthe Royal Statistical Society. Series B (Methodological),1974,36(2):192-236.
[22] Besag, Julian. Statistical Analysis of Non-Lattice Data [J]. Journal of the Royal StatisticalSociety. Series D (The Statistician),1975,24(3):179-195.
[23] Bessen, James. Open source software: Free provision of complex public goods [J]. Availableat SSRN588763,2005.
[24] Bishop, Yvonne M, Fienberg, Stephen E, Holland, Paul W. Discrete multivariate analysis:theory and practice [M]. Springer,2007.
[25] BlackDuck. Black Duck Software Estimates Development Cost of Open Source Software at$387Billion. from http://www.blackducksoftware.com/news/releases/2009-04-14
[26] Blondel, Vincent D, Guillaume, Jean-Loup, Lambiotte, Renaud, et al. Fast unfolding ofcommunities in large networks [J]. Journal of Statistical Mechanics: Theory and Experiment,2008,10): P10008.
[27] Bollobás, Béla, Riordan, Oliver, Spencer, Joel, et al. The degree sequence of a scale‐freerandom graph process [J]. Random Structures&Algorithms,2001,18(3):279-290.
[28] Bonaccorsi, A., Rossi, C. Comparing motivations of individual programmers and firms to takepart in the open source movement: From community to business [J]. Knowledge, Technology&Policy,2006,18(4):40–64.
[29] Borgatti, Stephen P.2-mode concepts in social network analysis [J]. Encyclopedia ofComplexity and System Science,2009,8279-8291.
[30] Borgatti, Stephen P., Everett, Martin G. Network analysis of2-mode data [J]. Social Networks,1997,19(3):243-269.
[31] Broder, Andrei, Kumar, Ravi, Maghoul, Farzin, et al. Graph structure in the web [J]. Computernetworks,2000,33(1):309-320.
[32] Carley, Kathleen M, Skillicorn, David. Special issue on analyzing large scale networks: TheEnron corpus [J]. Computational&Mathematical Organization Theory,2005,11(3):179-181.
[33] Chambers, John M. Facets of R [J]. The R Journal,2009,1(1):5-8.
[34] Chen, P, Redner, S. Community structure of the physical review citation network [J]. Journalof Informetrics,2010,4(3):278-290.
[35] Christakis, Nicholas A, Fowler, James H. The spread of obesity in a large social network over32years [J]. New England journal of medicine,2007,357(4):370-379.
[36] Clauset, Aaron, Moore, Cristopher, Newman, Mark EJ. Hierarchical structure and theprediction of missing links in networks [J]. Nature,2008,453(7191):98-101.
[37] Clauset, Aaron, Shalizi, Cosma Rohilla, Newman, M. E. J. Power-law distributions inempirical data [J]. SIAM review,2009,51(4):661-703.
[38] CMU, Department of Statistics. from http://www.stat.cmu.edu/
[39] Cohendet, Patrick, Creplet, Frederic, Dupouet, Olivier. Organisational innovation,communities of practice and epistemic communities: the case of Linux [M]. Economics withHeterogeneous Interacting Agents. Springer.2001:303-326.
[40] Croissant, Yves, Millo, Giovanni. Panel data econometrics in R: The plm package [J]. Journalof Statistical Software,2008,27(2):1-43.
[41] Crowston, K., Howison, J. The social structure of free and open source software development[J]. First Monday,2005,10(2-7).
[42] Crowston, Kevin, Howison, James. Hierarchy and centralization in free and open sourcesoftware team communications [J]. Knowledge, Technology&Policy,2006,18(4):65-85.
[43] Desmarais, Bruce A, Cranmer, Skyler J. Statistical inference for valued-edge networks: thegeneralized exponential random graph model [J]. PloS one,2012,7(1): e30136.
[44] Dorogovtsev, S. N., Goltsev, A. V., Mendes, J. F. F. Critical phenomena in complex networks[J]. Reviews of Modern Physics,2008,80(4):1275–1335.
[45] Durrett, Richard. Random graph dynamics [M]. Cambridge university press,2007.
[46] Eom Y-H, Fortunato S. Characterizing and Modeling Citation Dynamics [J]. PLoS ONE,2011,
[47] érdi, P, Makovi, Kinga, Somogyvári, Zoltán, et al. Prediction of emerging technologies basedon analysis of the US patent citation network [J]. Scientometrics,2013,95(1):225-242.
[48] Erdos, Paul, Rényi, A. On the evolution of random graphs [J]. Publ. Math. Inst. Hungar. Acad.Sci,1960,5:17-61.
[49] Faloutsos, Michalis, Faloutsos, Petros, Faloutsos, Christos. On power-law relationships of theinternet topology; proceedings of the ACM SIGCOMM Computer Communication Review, F,1999[C]. ACM.
[50] Fang, Yulin, Neufeld, Derrick. Understanding sustained participation in open source softwareprojects [J]. Journal of Management Information Systems,2009,25(4):9-50.
[51] Fortunato, S. Community detection in graphs [J]. Physics Reports,2010,486(3-5):75–174.
[52] Foster, J. G., Foster, D. V., Grassberger, P., et al. Edge direction and the structure of networks[J]. Proceedings of the National Academy of Sciences,2010,107(24):10815.
[53] Fox, John. Aspects of the Social Organization and Trajectory of the R Project [J]. The RJournal,2009,1(2):5-13.
[54] Frank, Ove, Strauss, David. Markov graphs [J]. Journal of the american Statistical association,1986,81(395):832-842.
[55] Gale, D. A theorem onflows in networks [J]. Pacific J. Math,1957,7:1073–1082.
[56] Gao, Yongqin, Freeh, Vince, Madey, Greg. Analysis and modeling of the open source softwarecommunity [J]. NAACSOS, Pittsburgh,2003,
[57] Ghosh, Rishab Aiyer. Interviews with Linus Torvalds: What motivates software developers [J].First Monday,1998,3(2).
[58] Ghosh, Rishab Aiyer. Economic impact of open source software on innovation and thecompetitiveness of the Information and Communication Technologies (ICT) sector in the EU[J].2007,
[59] Girvan, Michelle, Newman, M. E. J. Community structure in social and biological networks[J]. Proceedings of the National Academy of Sciences,2002,99(12):7821-7826.
[60] Godfrey, Michael W, Tu, Qiang. Evolution in open source software: A case study; proceedingsof the Software Maintenance,2000. Proceedings. International Conference on, F,2000[C].IEEE.
[61] Goldenberg, Anna, Moore, Andrew. Tractable learning of large Bayes net structures fromsparse data; proceedings of the Proceedings of the twenty-first international conference onMachine learning, F,2004[C]. ACM.
[62] Gonzalez-Barahona, Jesus M, Robles, Gregorio, Andradas-Izquierdo, Roberto, et al.Geographic origin of libre software developers [J]. Information Economics and Policy,2008,20(4):356-363.
[63] Guillaume, Jean-Loup, Latapy, Matthieu. Bipartite structure of all complex networks [J].Information processing letters,2004,90(5):215-221.
[64] Guillaume, Jean-Loup, Latapy, Matthieu. Bipartite graphs as models of complex networks [J].Physica A: Statistical Mechanics and its Applications,2006,371(2):795-813.
[65] Hammersley, J. M., Clifford, P. Markov field on finite graphs and lattices [J]. Unpublished,1971,
[66] Handcock, Mark S, Robins, Garry, Snijders, T. A. B., et al. Assessing degeneracy in statisticalmodels of social networks [M]. Working paper.2003.
[67] Hann, I. H., Roberts, J., Slaughter, S., et al. Economic incentives for participating in opensource software projects [M]. Proceedings of the Twenty-Third International Conference onInformation Systems.2002:365–372.
[68] Hann, Il-Horn, Roberts, Jeff, Slaughter, Sandra. Why developers participate in open sourcesoftware projects: An empirical investigation [J].2004,
[69] Hanneke, Steve, Fu, Wenjie, Xing, Eric P. Discrete temporal models of social networks [J].Electronic Journal of Statistics,2010,4:585-605.
[70] Hars, Alexander, Ou, Shaosong. Working for free? Motivations of participating in open sourceprojects; proceedings of the System Sciences,2001. Proceedings of the34th Annual HawaiiInternational Conference on, F,2001[C]. IEEE.
[71] Harvard, University. statistics courses. from http://www.registrar.fas.harvard.edu/statistics
[72] Hauck Jr, W. W., Donner, A. Wald's test as applied to hypotheses in logit analysis [J]. Journalof the American Statistical Association,1977,72):851-853.
[73] Hayek, Friedrich August. The Use of Knowledge in Soceity [J]. The American EconomicReview,1945,35(4):519-530.
[74] Haythornthwaite, Caroline. Crowds and communities: Light and heavyweight models of peerproduction; proceedings of the System Sciences,2009. HICSS'09.42nd Hawaii InternationalConference on, F,2009[C]. IEEE.
[75] Henkel, Joachim. Selective revealing in open innovation processes: The case of embeddedLinux [J]. Research policy,2006,35(7):953-969.
[76] Holland, John M. Hidden order: How adaptation builds complexity[M]. Basic Books,1995.
[77] Holland, Paul W., Leinhardt, Samuel. An Exponential Family of Probability Distributions forDirected Graphs [J]. Journal of the American Statistical Association,1981,76(373):33.
[78] Holme, Petter, Kim, Beom Jun, Yoon, Chang No, et al. Attack vulnerability of complexnetworks [J]. Physical Review E,2002,65(5):056109.
[79] Hu, Xiaojun, Rousseau, Ronald, Chen, Jin. A new approach for measuring the value of patentsbased on structural indicators for ego patent citation networks [J]. Journal of the AmericanSociety for Information Science and Technology,2012,63(9):1834-1842.
[80] Hunt, Francis, Johnson, Paul. On the Pareto distribution of Sourceforge projects; proceedingsof the Proceedings of the Open Source Software Development Workshop, F,2002[C].
[81] Ihaka, Ross. A Brief History R: Past and Future History. fromhttp://cran.r-project.org/doc/html/interface98-paper/paper_2.html
[82] JoPP. Mission Statement.2012, from http://peerproduction.net/about/mission-statement/
[83] Katz, Leo. The distribution of the number of isolates in a social group [J]. The Annals ofMathematical Statistics,1952,23(2):271-276.
[84] KDnuggets. What Analytics, Data mining, Big Data software you used in the past12monthsfor a real project?, fromhttp://www.kdnuggets.com/polls/2012/analytics-data-mining-big-data-software.html
[85] Kitsak, Maksim, Gallos, Lazaros K, Havlin, Shlomo, et al. Identification of influentialspreaders in complex networks [J]. Nature Physics,2010,6(11):888-893.
[86] Kleinberg, Jon. The small-world phenomenon: an algorithm perspective; proceedings of theProceedings of the thirty-second annual ACM symposium on Theory of computing, F,2000
[C]. ACM.
[87] Kleinberg, Jon. Small-world phenomena and the dynamics of information [J]. Advances inneural information processing systems,2002,1:431-438.
[88] Kleinberg, Jon M. Navigation in a small world [J]. Nature,2000,406(6798):845-845.
[89] Koch, Stefan. Free/open source software development [M]. Igi Global,2005.
[90] Koskinen, Johan, Edling, Christofer. Modelling the evolution of a bipartite network—Peerreferral in interlocking directorates [J]. Social Networks,2012,34(3):309-322.
[91] Lakhani, K. R., Von Hippel, E. How open source software works [J]. Research policy,2003,32(6):923–943.
[92] Lancashire, David. Code, culture and cash: The fading altruism of open source development[J]. First Monday,2001,6(12):
[93] Lancichinetti, A., Radicchi, F., Ramasco, J. J. Finding statistically significant communities innetworks [J]. PloS one,2011,6(4): e18961.
[94] Lancichinetti, Andrea, Radicchi, Filippo, Ramasco, José J. Statistical significance ofcommunities in networks [J]. Physical Review E,2010,81(4):046110.
[95] Lerner, J., Pathak, P. A., Tirole, J. The dynamics of open-source contributors [J]. TheAmerican economic review,2006,96(2):114–118.
[96] Lerner, J., Tirole, J. The open source movement: Key research questions [J]. EuropeanEconomic Review,2001,45(4-6):819–826.
[97] Lerner, J., Tirole, J. Some simple economics of open source [J]. The journal of industrialeconomics,2002,50(2):197–234.
[98] Lerner, J., Tirole, J. The economics of technology sharing: open source and beyond [J].Journal of Economic Perspectives,2005,19(2):99–120.
[99] Leskovec, Jure, Kleinberg, Jon, Faloutsos, Christos. Graph evolution: Densification andshrinking diameters [J]. ACM Transactions on Knowledge Discovery from Data (TKDD),2007,1(1):2.
[100] Leuf, Bo, Cunningham, Ward. The Wiki way: quick collaboration on the Web [J].2001,
[101] Liben‐Nowell, David, Kleinberg, Jon. The link‐prediction problem for social networks [J].Journal of the American society for information science and technology,2007,58(7):1019-1031.
[102] Lipnack, Jessica. Virtual teams: Reaching across space, time, and organizations withtechnology [M]. Jeffrey Stamps,1997.
[103] Madey, G., Freeh, V., Tynan, R. The open source software development phenomenon: Ananalysis based on social network theory [M]. Americas Conference on Information Systems(AMCIS2002).2002.
[104] Mahadevan, P., Krioukov, D., Fall, K., et al. Systematic topology analysis and generationusing degree correlations [J]. ACM SIGCOMM Computer Communication Review,2006,36(4):135–146.
[105] Maslov, S., Sneppen, K. Specificity and stability in topology of protein networks [J]. Science,2002,296(5569):910.
[106] Maslov, S., Sneppen, K., Zaliznyak, A. Detection of topological patterns in complex networks:correlation profile of the internet [J]. Physica A: Statistical and Theoretical Physics,2004,333(529–540.
[107] McCallum, Andrew, Corrada-Emmanuel, Andres, Wang, Xuerui. Topic and role discovery insocial networks [J]. Computer Science Department Faculty Publication Series,2005,3.
[108] Mcfadden, D. Conditional Logit Analysis of Qualitative Choice Behavior [J]. Frontiers inEconometrics,1974,105-142.
[109] McKee, Terry A, McMorris, Fred R. Topics in intersection graph theory [M]. Siam,1999.
[110] McPherson, Amanda, Proffitt, Brian, Hale-Evans, Ron. Estimating the Total DevelopmentCost of a Linux Distribution [J]. The Linux Foundation,2008,
[111] Mercken, Liesbeth, Snijders, T. A. B., Steglich, Christian, et al. Dynamics of adolescentfriendship networks and smoking behavior: Social network analyses in six European countries[J]. Social science&medicine,2009,69(10):1506-1514.
[112] Milgram, Stanley. The small world problem [J]. Psychology today,1967,2(1):60-67.
[113] Milo, R., Itzkovitz, S., Kashtan, N., et al. Superfamilies of evolved and designed networks [J].Science,2004,303(5663):1538.
[114] Milo, R., Shen-Orr, S., Itzkovitz, S., et al. Network motifs: simple building blocks of complexnetworks [J]. Science,2002,298(5594):824.
[115] Mockus, Audris, Fielding, Roy T, Herbsleb, James. A case study of open source softwaredevelopment: the Apache server; proceedings of the Software Engineering,2000. Proceedingsof the2000International Conference on, F,2000[C]. IEEE.
[116] Moreno, Jacob Levy. Who shall survive?: A new approach to the problem of humaninterrelations [J].1934,
[117] Moreno, Yamir, Pastor-Satorras, Romualdo, Vespignani, Alessandro. Epidemic outbreaks incomplex heterogeneous networks [J]. The European Physical Journal B-Condensed Matterand Complex Systems,2002,26(4):521-529.
[118] Mostafavi, Sara, Ray, Debajyoti, Warde-Farley, David, et al. GeneMANIA: a real-timemultiple association network integration algorithm for predicting gene function [J]. GenomeBiol,2008,9(Suppl1): S4.
[119] Nagurney, Anna. On the relationship between supply chain and transportation networkequilibria: A supernetwork equivalence with computations [J]. Transportation Research Part E:Logistics and Transportation Review,2006,42(4):293-316.
[120] Nagurney, Anna, Cruz, Jose, Dong, June, et al. Supply chain networks, electronic commerce,and supply side and demand side risk [J]. European Journal of Operational Research,2005,164(1):120-142.
[121] Nagurney, Anna, Cruz, Jose, Matsypura, Dmytro. Dynamics of global supply chainsupernetworks [J]. Mathematical and Computer Modelling,2003,37(9):963-983.
[122] Nagurney, Anna, Dong, June. Supernetworks: decision-making for the information age [M].Elgar, Edward Publishing, Incorporated,2002.
[123] Nagurney, Anna, Toyasaki, Fuminori. Supply chain supernetworks and environmental criteria[J]. Transportation Research Part D: Transport and Environment,2003,8(3):185-213.
[124] Nakakoji, Kumiyo, Yamamoto, Yasuhiro. The co-evolution of systems and communities infree and open source software development [J]. Free/open source software development,2005,59.
[125] Newman, M. E. J. Scientific collaboration networks. I. Network construction and fundamentalresults [J]. Physical review E,2001a,64(1):016131.
[126] Newman, M. E. J. The structure of scientific collaboration networks [J]. Proceedings of theNational Academy of Sciences,2001b,98(2):404-409.
[127] Newman, M. E. J. Modularity and community structure in networks [J]. Proceedings of theNational Academy of Sciences,2006,103(23):8577-8582.
[128] Newman, M. E. J. Networks: an introduction [M]. Oxford University Press,2009.
[129] Newman, M. E. J. Assortative mixing in networks [J]. Physical Review Letters,2002,89(20):208701.
[130] Newman, M. E. J. The Structure and Function of Complex Networks [J]. SIAM Review,2003,45(2):167-256.
[131] Newman, M. E. J., Girvan, M. Finding and evaluating community structure in networks [J].Physical review E,2004,69(2):026113.
[132] Newman, M. E. J., Strogatz, S. H., Watts, D. J. Random graphs with arbitrary degreedistributions and their applications [J]. Physical Review E,2001,64(2):026118.
[133] Newman, M. E. J., Watts, D. J., Strogatz, S. H. Random graph models of social networks [J].Proceedings of the National Academy of Sciences of the United States of America,2002,99(Suppl1):2566.
[134] Nyman, Linus, Mikkonen, Tommi. To Fork or Not to Fork: Fork Motivations in SourceForgeProjects [M]. IGI Global.2011:1-9.
[135] O'Reilly, T. Lessons from open source software development [J]. Communications of theACM,1999,42(4):33-37.
[136] O’Madadhain, Joshua, Smyth, Padhraic, Adamic, Lada. Learning predictive models for linkformation; proceedings of the International Sunbelt Social Network Conference, F,2005[C].
[137] Opsahl, Tore. Triadic closure in two-mode networks: Redefining the global and localclustering coefficients [J]. Social Networks,2013,35(2):159-167.
[138] Oracle. fromhttp://www.oracle.com/technetwork/cn/database/options/advanced-analytics/r-enterprise/index.html
[139] Orman, Wafa Hakim. Giving it away for free? The nature of job-market signaling byopen-source software developers [J]. The BE Journal of Economic Analysis&Policy,2008,8(1):
[140] Ou, Ruiqiu., Yang, Jianmei. On structural properties of scale-free networks with finite size [J].Physica a-Statistical Mechanics and Its Applications,2012,391(3):887-894.
[141] Papadopoulos, Fragkiskos, Kitsak, Maksim, Serrano, M ángeles, et al. Popularity versussimilarity in growing networks [J]. Nature,2012,489(7417):537-540.
[142] Park, J., Newman, M. E. J. Statistical mechanics of networks [J]. Physical Review E,2004,70(6):066117.
[143] Pastor-Satorras, Romualdo, Vespignani, Alessandro. Complex networks: Patterns ofcomplexity [J]. Nat Phys,2010,6(7):480-481.
[144] Pattison, Philippa, Wasserman, Stanley. Logit models and logistic regressions for socialnetworks: II. Multivariate relations [J]. British Journal of Mathematical and StatisticalPsychology,1999,52(2):169-193.
[145] Pittel, Boris. On tree census and the giant component in sparse random graphs [J]. RandomStructures&Algorithms,1990,1(3):311-342.
[146] Raymond, E. The cathedral and the bazaar [J]. Knowledge, Technology&Policy,1999,12(3):23–49.
[147] RevolutionAnalytics. from http://www.revolutionanalytics.com/
[148] Rinaldo, Alessandro, Fienberg, Stephen E, Zhou, Yi. On the geometry of discrete exponentialfamilies with application to exponential random graph models [J]. Electronic Journal ofStatistics,2009,3(446-484).
[149] Ripley, Ruth, Boitmanis, Krists, Snijders, T.A.B.(2013). RSiena: Siena-SimulationInvestigation for Empirical Network Analysis (Version1.1-232). Retrieved fromhttp://CRAN.R-project.org/package=RSiena
[150] Robillard, Pierre N. The role of knowledge in software development [J]. Commun. ACM,1999,42(1):87-92.
[151] Robins, Garry, Alexander, Malcolm. Small worlds among interlocking directors: Networkstructure and distance in bipartite graphs [J]. Computational&Mathematical OrganizationTheory,2004,10(1):69-94.
[152] Robins, Garry, Pattison, Philippa, Wasserman, Stanley. Logit models and logistic regressionsfor social networks: III. Valued relations [J]. Psychometrika,1999,64(3):371-394.
[153] Robins, Garry, Pattison, Pip, Kalish, Yuval, et al. An introduction to exponential random graph(p*) models for social networks [J]. Social Networks,2007,29(2):173-191.
[154] Sampson, Samuel Franklin. A novitiate in a period of change: An experimental and case studyof social relationships [D]; Cornell University, September,1968.
[155] Sarkar, Purnamrita, Moore, Andrew W. Dynamic social network analysis using latent spacemodels [J]. ACM SIGKDD Explorations Newsletter,2005,7(2):31-40.
[156] Sarkar, Purnamrita, Siddiqi, Sajid M, Gordon, Geoffrey J. A latent space approach to dynamicembedding of co-occurrence data; proceedings of the International Conference on ArtificialIntelligence and Statistics, F,2007[C].
[157] Schweinberger, Michael. Statistical modelling of network panel data: Goodness of fit [J].British Journal of Mathematical and Statistical Psychology,2012,65(2):263-281.
[158] Seidman, Stephen B. Network structure and minimum degree [J]. Social networks,1983,5(3):269-287.
[159] Shah, Sonali K. Motivation, governance, and the viability of hybrid forms in open sourcesoftware development [J]. Management Science,2006,52(7):1000-1014.
[160] Simmel, George. The sociology of georg simmel [M]. SimonandSchuster. com,1950.
[161] Simon, Herbert A. On a class of skew distribution functions [J]. Biometrika,1955,42(3/4):425-440.
[162] Snijders, T. A. B. Stochastic actor-oriented models for network change [J]. Journal ofmathematical sociology,1996,21(1-2):149–172.
[163] Snijders, T. A. B. The statistical evaluation of social network dynamics [J]. Sociologicalmethodology,2001,31(1):361–395.
[164] Snijders, T. A. B. Statistical models for social networks [J]. Annual Review of Sociology,2011,37:131-153.
[165] Snijders, T. A. B., Van de Bunt, G. G., Steglich, C. E. G. Introduction to stochastic actor-basedmodels for network dynamics [J]. Social Networks,2010,32(1):44–60.
[166] Stallman, Richard M, Gay, Joshua. Free software, free society: Selected essays of Richard M.Stallman [M]. CreateSpace,2009.
[167] Steglich, C., Snijders, T. A. B., Pearson, M. Dynamic networks and behavior: Separatingselection from influence [J]. Sociological Methodology,2010,
[168] Steglich, Christian. Longitudinal network analysis involving special data types [M]. WorkshopSocial Network Analysis2011. University of Groningen.2011.
[169] Stol, Klaas-Jan, Babar, Muhammad Ali. Reporting empirical research in open source software:the state of practice [M]. Open Source Ecosystems: Diverse Communities Interacting.Springer.2009:156-169.
[170] Stouffer, Daniel B, Malmgren, R Dean, Amaral, Luís AN. Log-normal statistics in e-mailcommunication patterns [J]. arXiv preprint physics/0605027,2006,
[171] Stouffer, Daniel B, Malmgren, R Dean, Amaral, Lu s AN. Comment on Barabási [J]. Nature,2005,435(207).
[172] Strauss, David, Ikeda, Michael. Pseudolikelihood estimation for social networks [J]. Journalof the American Statistical Association,1990,85(409):204-212.
[173] Subramanyam, Ramanath, Xia, Mu. Free/Libre Open Source Software development indeveloping and developed countries: A conceptual framework with an exploratory study [J].Decision support systems,2008,46(1):173-186.
[174] TIOBE. TIOBE Programming Community Index for May2013. fromhttp://www.tiobe.com/index.php/content/paperinfo/tpci/index.html
[175] Torvalds, Linus. The linux edge [J]. Communications of the ACM,1999,42(4):38-39.
[176] Travers, Jeffrey, Milgram, Stanley. An experimental study of the small world problem [J].Sociometry,1969,425-443.
[177] Van De Bunt, Gerhard G, Van Duijn, Marijtje AJ, Snijders, T. A. B. Friendship networksthrough time: An actor-oriented dynamic statistical network model [J]. Computational&Mathematical Organization Theory,1999,5(2):167-192.
[178] Verspagen, Bart. Mapping technological trajectories as patent citation networks: a study on thehistory of fuel cell research [J]. Advances in Complex Systems,2007,10(01):93-115.
[179] Von Hippel, Eric. The sources of innovation [M]. Springer,2007.
[180] Wagstrom, Patrick Adam, Herbsleb, Jim, Carley, Kathleen. A social network approach tofree/open source software simulation; proceedings of the Proceedings First InternationalConference on Open Source Systems, F,2005[C].
[181] Wang, Jing, Hu, Michael Y, Shanker, Murali. Human agency, social networks, and FOSSproject success [J]. Journal of Business Research,2012,65(7):977-984.
[182] Wasserman, S., Pattison, P. Logit models and logistic regressions for social networks: I. Anintroduction to Markov graphs andp [J]. Psychometrika,1996,61(3):401–425.
[183] Wasserman, Stanley. Social network analysis: Methods and applications [M]. Cambridgeuniversity press,1994.
[184] Watts, D. J., Strogatz, S. H. Collective dynamics of ‘small-world’networks [J]. Nature,1998,393(6684):440–442.
[185] Wellman, Barry. An electronic group is virtually a social network [J]. Culture of the Internet,1997,4:179-205.
[186] West, Joel, O'Mahony, Siobhán. The Role of Participation Architecture in Growing SponsoredOpen Source Communities [J]. Industry and Innovation,2008,15(2):145-168.
[187] West, Joel, O'Mahony, Siobhan. Contrasting community building in sponsored and communityfounded open source projects; proceedings of the System Sciences,2005. HICSS'05.Proceedings of the38th Annual Hawaii International Conference on, F,2005[C]. IEEE.
[188] Wu, Chorng-Guang, Gerlach, James H, Young, Clifford E. An empirical analysis of opensource software developers’ motivations and continuance intentions [J]. Information&Management,2007,44(3):253-262.
[189] Xu, Bo, Jones, Donald R, Shao, Bingjia. Volunteers’ involvement in online community basedsoftware development [J]. Information&management,2009,46(3):151-158.
[190] Xu, Jin. Mining and modeling the open source software community [D]; University of NotreDame,2008.
[191] Xu, Jin., Christley, S., Madey, G. Application of social network analysis to the study of opensource software [J]. The economics of open source software development,2006,205–224.
[192] Yang, Jianmei, Lu, Lvping, Xie, Wangdan, et al. On competitive relationship networks: A newmethod for industrial competition analysis [J]. Physica A: Statistical Mechanics and itsApplications,2007,382(2):704-714.
[193] Yang, Jianmei, Zhuang, Dong, Xu, Xiao. The complex network analysis on service channelsof a bank and its management utility [J]. Dynamics of Continuous Discrete and ImpulsiveSystems Series B,2008,15(2):179.
[194] Yang, Xiaokai, Borland, Jeff. A Microeconomic Mechanism for Economic Growth [J]. Journalof Political Economy,1991,460-482.
[195] Ye, Yunwen, Kishida, Kouichi. Toward an understanding of the motivation of open sourcesoftware developers; proceedings of the Software Engineering,2003. Proceedings.25thInternational Conference on, F,2003[C]. IEEE.
[196] Zhang, Pei-Pei, Chen, Kan, He, Yue, et al. Model and empirical study on some collaborationnetworks [J]. Physica A: Statistical Mechanics and its applications,2006,360(2):599-616.
[197]阿尔温·托夫勒.第三次浪潮[M].北京:生活.读书.新知三联书店,2006.
[198]阿林杨格.报酬递增与经济进步[J].经济社会体制比较,1996,3):52-57.
[199]保罗·萨缪尔森,诺德豪斯.经济学（第17版）[M].北京:人民邮电出版社,2004.
[200]毕桥,方锦清.网络科学与统计物理方法[M].北京:北京大学出版社,2011.
[201]曾进群,杨建梅,陈泉.基于加权网络模型的开源软件社区大众生产结构研究[J].工业工程,2013,6):
[202]曾进群,杨建梅,陈泉, et al.基于零模型的开源社区大众生产合作网络结构研究[J].华南理工大学学报(社会科学版),2013,4):
[203]常静.百度百科及开源社区参与者的动机与行为关系的实证研究[D];华南理工大学,2009.
[204]陈关荣,汪小帆,李翔. Introduction to Complex Networks: Models, Structures andDynamics [M].北京:高等教育出版社,2012.
[205]陈泉,杨建梅,曾进群.零模型及其在复杂网络研究中的应用[J].复杂系统与复杂性科学,2013,10(1):8-17.
[206]方锦清,汪小帆,郑志刚, et al.一门崭新的交叉科学:网络科学(上)[J].物理学进展,2007,27(3):239-343.
[207]方锦清,汪小帆,郑志刚, et al.一门崭新的交叉科学:网络科学(下篇)[J].物理学进展,2007,27(004):361–448.
[208]哈耶克.致命的自负[M].北京:中国社会科学出版社,2000.
[209]哈耶克.个人主义与经济秩序[M].北京:生活.读书.新知三联书店,2003.
[210]杰夫·豪.众包:大众力量缘何推动商业未来[M].北京:中信出版社,2009.
[211]卡尔波普尔.论三个世界[M].通过知识获得解放.北京;中国美术学院出版社.1998.
[212]克里斯·安德森.长尾理论[M].北京:中信出版社,2006.
[213]李欣荣.百度百科与谷歌开源社区比较研究[D];华南理工大学,2011.
[214]罗宾斯.管理学[M].北京:人民大学出版社,2004.
[215]马歇尔.经济学原理[M].北京:商务印书馆,1965.
[216]欧瑞秋.网络博弈视角下的中国汽车企业对抗互动研究[D],2010.
[217]潘向东.开源社区稳定性研究[D].广州;华南理工大学,2009.
[218]史定华.网络度分布理论[M].北京:高等教育出版社,2011.
[219]唐·泰普斯科特,安东尼·D·威廉姆斯.维基经济学:大规模协作如何改变一切[M].北京:中国青年出版社,2007.
[220]汪小帆,李翔,陈关荣.网络科学导论[M].北京:高等教育出版社,2012.
[221]王志平,王众托.超网络理论及其应用[M].科学出版社,2008.
[222]谢益辉,郑冰.(2008). R语言的历史背景、发展历程和现状. Paper presented at the1stChina R Conference,北京.
[223]亚当斯密.国民财富的性质和原因的研究[M].北京:商务印书馆,1988.
[224]杨建梅.复杂网络与社会网络研究范式的比较[J].系统工程理论与实践,2010,11.
[225]于洋.组织知识管理中的知识超网络研究[D];大连理工大学,2009.
[226]周连强. Android开源操作系统的大众生产研究[D].广州;华南理工大学,2011.