社交媒体个人数据资源的多边市场竞争研究——基于k核凝聚子网的实证解析
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摘要
基于2016～2017年期间连续的用户活跃度相关指标和社交媒体平台API开放状态,构建社交媒体平台链态矩阵,并采取社会网络分析(SNA)方法中的k核分析方法,通过网络模块度、网络聚集系数和网络密度的测定,对选取的31个具有代表性的中国社交媒体平台个人数据链接网络(PDCN)的汇聚结构进行分析。实证结果显示,样本网络存在网络聚集系数和网络密度较高的k核子网络,处于k核子网络中的平台构成了一个完整的数据资源配置网络。研究认为目前中国社交媒体平台个人数据链接网络汇聚较为显著,社交媒体产业结构性资源的个人数据资源的汇聚直接影响市场竞争状态,处于个人数据链接网络结构中心位置的平台对基于数据资源的多边市场竞争结构具有较强的控制力。
Base on the continuous user activity related indicators and social media platform API openness from 2016 to 2017, this research adopts the social network analysis method to study the convergence status of Personal Data Connectedness Network(PDCN) of the selected China's social media platforms(N=31). The analysis index includes k-core decomposition, modularity test, clustering coefficient, and density test. According to the empirical study, this research has found that the platforms in k-core network of the sample network have sufficiency impact of the allocation of the personal data resource. The result shows that there is a centralized structure of the market competition of China's social media platform. The platforms that occupy the centralized position of the PDCN have greater impact of controlling the data resource allocation.
Base on the continuous user activity related indicators and social media platform API openness from 2016 to 2017, this research adopts the social network analysis method to study the convergence status of Personal Data Connectedness Network(PDCN) of the selected China's social media platforms(N=31). The analysis index includes k-core decomposition, modularity test, clustering coefficient, and density test. According to the empirical study, this research has found that the platforms in k-core network of the sample network have sufficiency impact of the allocation of the personal data resource. The result shows that there is a centralized structure of the market competition of China's social media platform. The platforms that occupy the centralized position of the PDCN have greater impact of controlling the data resource allocation.
引文
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[20] Boccaletti S,Ivanchenko M,Latora V,et al.Detecting Complex Network Modularity by Dynamical Clustering[J].Physical Review E,2007,75(4):1-4.
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[22] Zhang H,Zhao H,Cai W,et al.Using the K-Core Decomposition to Analyze the Static Structure of Large-Scale Software Systems[J].The Journal of Supercomputing,2010,53(2):352-369.
[23] Csardi G,Nepusz T.The Igraph Software Package for Complex Network Research[J].International Journal of Complex Systems,2006,1695(5):1-9.
[24] Butts C T.Social Network Analysis with Sna[J].Journal of Statistical Software,2008,24(6):1-51.
[25] Batagelj V,Zaversnik M.An O(M) Algorithm for Cores Decomposition of Networks[J].Advances in Data Analysis and Classification,2011,5(2):129-145.
[26] Leskovec J,Lang K J,Dasgupta A,et al.Community Structure in Large Networks:Natural Cluster Sizes and the Absence of Large Well-Defined Clusters[J].Internet Mathematics,2009,6(1):29-123.
[27] Newman M E.Modularity and Community Structure in Networks[J].Proceedings of the National Academy of Sciences,2006,103(23):8577-8582.
[28] Blondel V D,Guillaume J-L,Lambiotte R,et al.Fast Unfolding of Communities in Large Networks[J].Journal of Statistical Mechanics:Theory and Experiment,2008,10(10):1-12.
[29] 中国互联网数据平台.第42次中国互联网络发展状况统计报告[R].北京 :中国互联网数据平台,2018.1-102.
①所谓的积极反馈与“马太效应”相似,是指经济活动中使强者变得更强,使弱者变得更弱的现象。同时,积极反馈是一个动态的过程。积极反馈往往会导致极端结果的出现。
①截止2016年6月30日,中国互联网数据平台尚未对微信及知乎的相关数据进行统计,因此本研究采用了知名的互联网数据统计机构易观千帆的统计数据,数据来源:http://zhishu.analysys.cn/。
②覆盖人群数及比例为2016年1月1日至6月30日的月度平均值。
(1)用户规模数据获取时间:2017年1月,数据来源:http://www.cnidp.cn/ 和http://zhishu.analysys.cn/。
[2] Parker G,Van Alstyne M.Innovation,Openness,and Platform Control[J].Management Science,2018,64(7):3015-3032.
[3] OECD.Data-Driven Innovation Big Data for Growth and Well-Being[M].Paris:OECD Publishing,2015.4-6.
[4] Hagiu A,Wright J.Multi-Sided Platforms[J].International Journal of Industrial Organization,2015,43(C):162-174.
[5] Nambisan S,Lyytinen K,Majchrzak A,et al.Digital Innovation Management:Reinventing Innovation Management Research in a Digital World[J].MIS Quarterly,2017,41(1):223–238.
[6] Stolfo S J,Bellovin S M,Keromytis A D,et al.Insider Attack and Cyber Security Beyond the Hacker[M].Boston MA:Springer US,2008.9-14.
[7] Castells M,Cardoso G.The Network Society:From Knowledge to Policy[M].Washington,DC:Johns Hopkins Center for Transatlantic Relations,2006.23-65.
[8] 房嘉财.连接(移动互联网时代的商业智慧密码)[M].北京:机械工业出版社,2015.1-176.
[9] 涂子沛.大数据及其成因[J].科学与社会,2014(1):14-26.
[10] 柯惠新,黄可.从平面化(2D)到立体化(3D)——对新媒体时代受众测量的思考[J].现代传播(中国传媒大学学报),2011(10):103-106.
[11] Gerlitz C,Helmond A.The Like Economy:Social Buttons and the Data-Intensive Web[J].New Media & Society,2013,15(8):1348-1365.
[12] Hamari J,Sj?klint M,Ukkonen A.The Sharing Economy:Why People Participate in Collaborative Consumption[J].Journal of the Association for Information Science and Technology,2015,67(9):2047–2059.
[13] Olson J A.Data as a Service:Are We in the Clouds?[J].Journal of Map & Geography Libraries,2009,6(1):76-78.
[14] Curry E.The Big Data Value Chain:Definitions,Concepts,and Theoretical Approaches.New Horizons for a Data-Driven Economy[M].Cham:Springer,2016.29-37.
[15] Lyko K,Nitzschke M,Ngomo A-C N.Big Data Acquisition.New Horizons for a Data-Driven Economy[M].Cham:Springer,2016.39-61.
[16] Katz M,Sallet J.Multisided Platforms and Antitrust Enforcement[J].Yale Law Journal,2018,127(7):2142-2175.
[17] Economides N.The Microsoft Antitrust Case[J].Journal of Industry,Competition and Trade,2001,1(1):7-39.
[18] Saaskilahti P.Monopoly Pricing of Social Goods[J].International Journal of the Economics of Business,2015,22(3):429-448.
[19] Graef I,Wahyuningtyas S Y,Valcke P.Assessing Data Access Issues in Online Platforms[J].Telecommunications Policy,2015,39(5):375-387.
[20] Boccaletti S,Ivanchenko M,Latora V,et al.Detecting Complex Network Modularity by Dynamical Clustering[J].Physical Review E,2007,75(4):1-4.
[21] Leblanc G,Secco R,Kostic M.The Measurement Instrumentation Sensors Handbook:Viscosity Measurement [M].Washington D.C:CRC Press,1999.891-913.
[22] Zhang H,Zhao H,Cai W,et al.Using the K-Core Decomposition to Analyze the Static Structure of Large-Scale Software Systems[J].The Journal of Supercomputing,2010,53(2):352-369.
[23] Csardi G,Nepusz T.The Igraph Software Package for Complex Network Research[J].International Journal of Complex Systems,2006,1695(5):1-9.
[24] Butts C T.Social Network Analysis with Sna[J].Journal of Statistical Software,2008,24(6):1-51.
[25] Batagelj V,Zaversnik M.An O(M) Algorithm for Cores Decomposition of Networks[J].Advances in Data Analysis and Classification,2011,5(2):129-145.
[26] Leskovec J,Lang K J,Dasgupta A,et al.Community Structure in Large Networks:Natural Cluster Sizes and the Absence of Large Well-Defined Clusters[J].Internet Mathematics,2009,6(1):29-123.
[27] Newman M E.Modularity and Community Structure in Networks[J].Proceedings of the National Academy of Sciences,2006,103(23):8577-8582.
[28] Blondel V D,Guillaume J-L,Lambiotte R,et al.Fast Unfolding of Communities in Large Networks[J].Journal of Statistical Mechanics:Theory and Experiment,2008,10(10):1-12.
[29] 中国互联网数据平台.第42次中国互联网络发展状况统计报告[R].北京 :中国互联网数据平台,2018.1-102.
①所谓的积极反馈与“马太效应”相似,是指经济活动中使强者变得更强,使弱者变得更弱的现象。同时,积极反馈是一个动态的过程。积极反馈往往会导致极端结果的出现。
①截止2016年6月30日,中国互联网数据平台尚未对微信及知乎的相关数据进行统计,因此本研究采用了知名的互联网数据统计机构易观千帆的统计数据,数据来源:http://zhishu.analysys.cn/。
②覆盖人群数及比例为2016年1月1日至6月30日的月度平均值。
(1)用户规模数据获取时间:2017年1月,数据来源:http://www.cnidp.cn/ 和http://zhishu.analysys.cn/。