An Integrated Method for Cooperation Prediction in Complex Standard Networks
Abstract
:1. Introduction
2. Literature Review
2.1. Cooperation Network Analysis
2.2. Link Prediction
3. Methodology
3.1. Problem Statement and Research Framework
3.2. A Multi-Layer Network Model
3.3. Text Similarity Analysis
3.4. Link Prediction Hypotheses
3.4.1. Standards Association and Cooperation Potential
3.4.2. Citation Degree and Cooperation Potential
3.4.3. Topological Connectivity and Cooperation Potential
4. Case Study
4.1. Data Collection and Resources
4.2. AUC Validation under Metrics Integration
4.3. Cooperation Prediction with Neural Network
5. Discussion and Practical Implications
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
- Brunsson, N.; Jacobsson, B. A World of Standards; Oxford University Press: Oxford, UK, 2002. [Google Scholar]
- Abbott, K.W.; Snidal, D. The governance triangle: Regulatory standards institutions and the shadow of the state. In The Spectrum of International Institutions; Routledge: London, UK, 2021; pp. 52–91. [Google Scholar]
- Blind, K.; Heß, P. Stakeholder perceptions of the role of standards for addressing the sustainable development goals. Sustain. Prod. Consum. 2023, 37, 180–190. [Google Scholar] [CrossRef]
- Wei, Y.; Wang, C.; Qi, Y.; Wang, H.; Li, F.; Chen, F. Dynamic evaluation of the influence of drafting units in china’s air quality standards network. J. Clean. Prod. 2020, 252, 119834. [Google Scholar] [CrossRef]
- Johnston, K.; Farah, L.; Ghuman, H.; Baker, J. To draft or not to draft? A systematic review of North American sports’ entry draft. Scand. J. Med. Sci. Sport. 2022, 32, 4–17. [Google Scholar] [CrossRef]
- Lin, S.; Razzaq, A.; Yi, K. Heterogenous influence of productive capacities pillars and natural resources on ecological sustainability in developing Belt and Road host countries. Resour. Policy 2023, 85, 103776. [Google Scholar] [CrossRef]
- Jiang, H.; Sun, S.; Xu, H.; Zhao, S.; Chen, Y. Enterprises’ network structure and their technology standardization capability in industry 4.0. Syst. Res. Behav. Sci. 2020, 37, 749–765. [Google Scholar] [CrossRef]
- Seaman, J. China and the new geopolitics of technical standardization. Notes L’Ifri 2020, 34, 20–21. [Google Scholar]
- Bundgaard, A.M.; Huulgaard, R.D. The role of standards in support of material efficiency requirements under the Ecodesign Directive. J. Clean. Prod. 2023, 385, 135599. [Google Scholar] [CrossRef]
- Wen, J.; Qualls, W.J.; Zeng, D. Standardization alliance networks, standard-setting influence, and new product outcomes. J. Prod. Innov. Manag. 2020, 37, 138–157. [Google Scholar] [CrossRef]
- Hyland, J.; Karlsson, M. Towards a management system standard for innovation. J. Innov. Manag. 2021, 9, XI–XIX. [Google Scholar] [CrossRef]
- Vinke-de Kruijf, J.; Bressers, H.; Augustijn, D.C.M. How social learning influences further collaboration: Experiences from an international collaborative water project. Ecol. Soc. 2014, 19, 61. [Google Scholar] [CrossRef]
- Kayyali, M. An overview of quality assurance in higher education: Concepts and frameworks. Int. J. Manag. Sci. Innov. Technol. 2023, 4, 1–4. [Google Scholar]
- McGreal, R.; Mackintosh, W.; Cox, G.; Olcott, D., Jr. Bridging the gap: Micro-credentials for development: UNESCO chairs policy brief form-under the III world higher education conference (WHEC 2021) type: Collective X. Int. Rev. Res. Open Distrib. Learn. 2022, 23, 288–302. [Google Scholar] [CrossRef]
- Liang, X.; Wang, Y.; Yang, M. Systemic modeling and prediction of port container throughput using hybrid link analysis in complex networks. Systems 2024, 12, 23. [Google Scholar] [CrossRef]
- Lü, L.; Zhou, T. Link prediction in complex networks: A survey. Phys. Stat. Mech. Its Appl. 2011, 390, 1150–1170. [Google Scholar] [CrossRef]
- Zhang, M.; Chen, Y. Link prediction based on graph neural networks. Adv. Neural Inf. Process. Syst. 2018, 31, 5171–5181. [Google Scholar]
- Guan, Y.; Li, L.; Liu, C. Resilience characteristics and driving mechanism of urban collaborative innovation network—A case study of china’s new energy vehicle industry. Systems 2023, 11, 214. [Google Scholar] [CrossRef]
- Narayanan, A.; Shi, E.; Rubinstein, B.I. Link prediction by de-anonymization: How we won the kaggle social network challenge. In Proceedings of the 2011 International Joint Conference on Neural Networks, San Jose, CA, USA, 31 July–5 August 2011; IEEE: Piscataway, NJ, USA, 2011; pp. 1825–1834. [Google Scholar]
- Cho, H.; Yu, Y. Link prediction for interdisciplinary collaboration via co-authorship network. Soc. Netw. Anal. Min. 2018, 8, 25. [Google Scholar] [CrossRef]
- Abbas, K.; Abbasi, A.; Dong, S.; Niu, L.; Yu, L.; Chen, B.; Cai, S.-M.; Hasan, Q. Application of network link prediction in drug discovery. Bmc Bioinform. 2021, 22, 187. [Google Scholar] [CrossRef]
- Liben-Nowell, D.; Kleinberg, J. The link prediction problem for social networks. In Proceedings of the Twelfth International Conference on Information and Knowledge Management, New Orleans, LA, USA, 3–8 November 2003; pp. 556–559. [Google Scholar]
- Yu, Q.; Long, C.; Lv, Y.; Shao, H.; He, P.; Duan, Z. Predicting co-author relationship in medical co-authorship networks. PLoS ONE 2014, 9, e101214. [Google Scholar] [CrossRef]
- Ayoub, J.; Lotfi, D.; El Marraki, M.; Hammouch, A. Accurate link prediction method based on path length between a pair of unlinked nodes and their degree. Soc. Netw. Anal. Min. 2020, 10, 9. [Google Scholar] [CrossRef]
- Zangari, L.; Mandaglio, D.; Tagarelli, A. Link prediction on multilayer networks through learning of within-layer and across-layer node-pair structural features and node embedding similarity. In Proceedings of the ACM on Web Conference, Singapore, 13–17 May 2024; pp. 924–935. [Google Scholar]
- Liu, P.; Xia, H. Structure and evolution of co-authorship network in an interdisciplinary research field. Scientometrics 2015, 103, 101–134. [Google Scholar] [CrossRef]
- Fagan, J.; Eddens, K.S.; Dolly, J.; Vanderford, N.L.; Weiss, H.; Levens, J.S. Assessing research collaboration through co-authorship network analysis. J. Res. Adm. 2018, 49, 76–99. [Google Scholar] [PubMed]
- Purwitasari, D.; Fatichah, C.; Sumpeno, S.; Steglich, C.; Purnomo, M.H. Identifying collaboration dynamics of bipartite author-topic networks with the influences of interest changes. Scientometrics 2020, 122, 1407–1443. [Google Scholar] [CrossRef]
- Ajiferuke, I.; Grácio, M.C.C.; Yang, S. Research Collaboration and Networks: Characteristics, Evolution and Trends. Front. Res. Metrics Anal. 2021, 6, 690986. [Google Scholar] [CrossRef] [PubMed]
- Newman, M.E. The structure of scientific collaboration networks. Proc. Natl. Acad. Sci. USA 2001, 98, 404–409. [Google Scholar] [CrossRef] [PubMed]
- Leydesdorff, L.; Wagner, C.S.; Bornmann, L. Betweenness and diversity in journal citation networks as measures of interdisciplinarity—A tribute to eugene garfield. Scientometrics 2018, 114, 567–592. [Google Scholar] [CrossRef] [PubMed]
- Zhou, J.; Zeng, A.; Fan, Y.; Di, Z. Identifying important scholars via directed scientific collaboration networks. Scientometrics 2018, 114, 1327–1343. [Google Scholar] [CrossRef]
- Ueda, R.; Nishizaki, Y.; Homma, Y.; Devos, P.; Sanada, S. The relationship between contributions of authors and author order. J. Gen. Fam. Med. 2021, 22, 361–362. [Google Scholar] [CrossRef] [PubMed]
- Li, A.; Zhou, L.; Su, Q.; Cornelius, S.P.; Liu, Y.-Y.; Wang, L.; Levin, S.A. Evolution of cooperation on temporal networks. Nat. Commun. 2020, 11, 2259. [Google Scholar] [CrossRef]
- Kim, J.; Diesner, J. Coauthorship networks: A directed network approach considering the order and number of coauthors. J. Assoc. Inf. Sci. Technol. 2015, 66, 2685–2696. [Google Scholar] [CrossRef]
- Ji, P.; Jin, J.; Ke, Z.T.; Li, W. Co-citation and co-authorship networks of statisticians. J. Bus. Econ. Stat. 2022, 40, 469–485. [Google Scholar] [CrossRef]
- De Noni, I.; Orsi, L.; Belussi, F. The role of collaborative networks in supporting the innovation performances of lagging-behind european regions. Res. Policy 2018, 47, 1–13. [Google Scholar] [CrossRef]
- Lin, R.; Lu, Y.; Zhou, C.; Li, B. Rethinking individual technological innovation: Cooperation network stability and the contingent effect of knowledge network attributes. J. Bus. Res. 2022, 144, 366–376. [Google Scholar] [CrossRef]
- Zhu, J.; Hong, J.; Hughes, J.G. Using markov chains for link prediction in adaptive web sites. In Conference on Soft Issues in the Design, Development, and Operation of Computing Systems; Springer: Berlin/Heidelberg, Germany, 2002; pp. 60–73. [Google Scholar]
- Martínez, V.; Berzal, F.; Cubero, J.C. A survey of link prediction in complex networks. ACM Comput. Surv. 2016, 49, 1–33. [Google Scholar] [CrossRef]
- Feng, X.; Zhao, J.; Xu, K. Link prediction in complex networks: A clustering perspective. Eur. Phys. J. 2012, 85, 1–9. [Google Scholar] [CrossRef]
- Daud, N.N.; Ab Hamid, S.H.; Saadoon, M.; Sahran, F.; Anuar, N.B. Applications of link prediction in social networks: A review. J. Netw. Comput. Appl. 2020, 166, 102716. [Google Scholar] [CrossRef]
- Yao, L.; Wang, L.; Pan, L.; Yao, K. Link prediction based on common-neighbors for dynamic social network. Procedia Comput. Sci. 2016, 83, 82–89. [Google Scholar] [CrossRef]
- Hasan, M.A.; Zaki, M.J. A survey of link prediction in social networks. In Social Network Data Analytics; Springer: Boston, MA, USA, 2011; pp. 243–275. [Google Scholar]
- Prakoso, D.W.; Abdi, A.; Amrit, C. Short text similarity measurement methods: A review. Soft Comput. 2021, 25, 4699–4723. [Google Scholar] [CrossRef]
- Lim, M.; Abdullah, A.; Jhanjhi, N.; Khan, M.K.; Supramaniam, M. Link prediction in time-evolving criminal network with deep reinforcement learning technique. IEEE Access 2019, 7, 184797–184807. [Google Scholar] [CrossRef]
- Lei, K.; Qin, M.; Bai, B.; Zhang, G.; Yang, M. Gcn-gan: A non-linear temporal link prediction model for weighted dynamic networks. In Proceedings of the IEEE INFOCOM 2019—IEEE Conference on Computer Communications, Paris, France, 29 April–2 May 2019; IEEE: Piscataway, NJ, USA, 2019; pp. 388–396. [Google Scholar]
- Cao, Z.; Zhang, Y.; Guan, J.; Zhou, S.; Wen, G. A chaotic ant colony optimized link prediction algorithm. IEEE Trans. Syst. Man Cybern. Syst. 2019, 51, 5274–5288. [Google Scholar] [CrossRef]
- Ghorbanzadeh, H.; Sheikhahmadi, A.; Jalili, M.; Sulaimany, S. A hybrid method of link prediction in directed graphs. Expert Syst. Appl. 2021, 165, 113896. [Google Scholar] [CrossRef]
- Aizawa, A. An information-theoretic perspective of tf–idf measures. Inf. Process. Manag. 2003, 39, 45–65. [Google Scholar] [CrossRef]
- Hooton, R.D. Bridging the gap between research and standards. Cem. Concr. Res. 2008, 38, 247–258. [Google Scholar] [CrossRef]
- Winman, T. Standardization and Professional Knowledge in Integration Work. Int. J. Soc. Work. Hum. Serv. Pract. 2021, 8, 17–26. [Google Scholar] [CrossRef]
- Soares, A.d.J.; Pereira, R.B.; Baldam, R.d.L.; de Francisco, A.C. Creation of organizational knowledge through a model of standardization of production systems in the paper industry. J. Knowl. Manag. 2023, 27, 426–459. [Google Scholar] [CrossRef]
- Arsawan, I.W.E.; Koval, V.; Rajiani, I.; Rustiarini, N.W.; Supartha, W.G.; Suryantini, N.P.S. Leveraging knowledge sharing and innovation culture into SMEs sustainable competitive advantage. Int. J. Product. Perform. Manag. 2022, 71, 405–428. [Google Scholar] [CrossRef]
- Kallestrup, M. Stakeholder participation in European standardization: A mapping and an assessment of three categories of regulation. Leg. Issues Econ. Integr. 2017, 44, 381–393. [Google Scholar] [CrossRef]
- Barnett, J.M. Antitrust overreach: Undoing cooperative standardization in the digital economy. Mich. Tech. L. Rev. 2018, 25, 163. [Google Scholar] [CrossRef]
- Taşkın, Z.; Al, U. Standardization problem of author affiliations in citation indexes. Scientometrics 2014, 98, 347–368. [Google Scholar] [CrossRef]
- Hota, P.K.; Subramanian, B.; Narayanamurthy, G. Mapping the intellectual structure of social entrepreneurship research: A citation/co-citation analysis. J. Bus. Ethics 2020, 166, 89–114. [Google Scholar] [CrossRef]
- Petersen, A.M.; Arroyave, F.; Pammolli, F. The disruption index suffers from citation inflation and is confounded by shifts in scholarly citation practice. arXiv 2024, arXiv:2406.15311. [Google Scholar] [CrossRef]
- Weitzel, T.; Beimborn, D.; König, W. A unified economic model of standard diffusion: The impact of standardization cost, network effects, and network topology. MIS Q. 2006, 30, 489–514. [Google Scholar] [CrossRef]
- Gienapp, L.; Kruckenberg, C.; Burghardt, M. Topological properties of music collaboration networks: The case of Jazz and Hip Hop. DHQ Digit. Humanit. Q. 2021, 15. [Google Scholar]
- Choi, E.W.; Özer, Ö.; Zheng, Y. Network trust and trust behaviors among executives in supply chain interactions. Manag. Sci. 2020, 66, 5823–5849. [Google Scholar] [CrossRef]
- Zhao, J. Coupling open innovation: Network position, knowledge integration ability, and innovation performance. J. Knowl. Econ. 2023, 14, 1538–1558. [Google Scholar] [CrossRef]
- Hilmersson, F.P.; Hilmersson, M. Networking to accelerate the pace of SME innovations. J. Innov. Knowl. 2021, 6, 43–49. [Google Scholar] [CrossRef]
- Huang, J.; Ling, C.X. Using auc and accuracy in evaluating learning algorithms. IEEE Trans. Knowl. Data Eng. 2005, 17, 299–310. [Google Scholar] [CrossRef]
- Cai, L.; Li, J.; Wang, J.; Ji, S. Line graph neural networks for link prediction. IEEE Trans. Pattern Anal. Mach. Intell. 2021, 44, 5103–5113. [Google Scholar] [CrossRef]
- Koren, Y.; Bell, R.; Volinsky, C. Matrix factorization techniques for recommender systems. Computer 2009, 42, 30–37. [Google Scholar] [CrossRef]
- Lu, Y.; Gao, M.; Liu, H.; Liu, Z.; Yu, W.; Li, X.; Jiao, P. Neighborhood overlap-aware heterogeneous hypergraph neural network for link prediction. Pattern Recognit. 2023, 144, 109818. [Google Scholar] [CrossRef]
- Blind, K.; Kenney, M.; Leiponen, A.; Simcoe, T. Standards and innovation: A review and introduction to the special issue. Res. Policy 2023, 52, 104830. [Google Scholar] [CrossRef]
- He, L.; Bai, L.; Yang, X.; Du, H.; Liang, J. High-order graph attention network. Inf. Sci. 2023, 630, 222–234. [Google Scholar] [CrossRef]
- Wang, J.; Niu, X.; Zhang, L.; Liu, Z.; Huang, X. A wind speed forecasting system for the construction of a smart grid with two-stage data processing based on improved ELM and deep learning strategies. Expert Syst. Appl. 2024, 241, 122487. [Google Scholar] [CrossRef]
Main Symbols | Explanations |
---|---|
Adjacent matrix representing the citation relationship | |
Cooperation relationship matrix between drafters | |
The TF-IDF value of a keyword k in a specific standard s | |
TAI | Total association intensity |
MAI | Maximal association intensity |
TCI | Total citation intensity |
MCI | Maximal citation intensity |
CN | Common Neighbors |
LP | Local Path |
SR | SimRank |
Similarity degree between standards and , | |
Citation index between standards and , |
Index | CN | LP | SR | TAI | MAI | TCI | MCI |
---|---|---|---|---|---|---|---|
AUC value | 0.72 | 0.68 | 0.7 | 0.75 | 0.73 | 0.69 | 0.7 |
Metrics | Case 1 | Case 2 | Case 3 | Case 4 | Case 5 | Case 6 | Case 7 | Case 8 | Case 9 | Case 10 |
---|---|---|---|---|---|---|---|---|---|---|
TAI | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 1 |
MAI | 1 | 0 | 0 | 1 | 1 | 0 | 1 | 1 | 1 | 1 |
TCI | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 1 |
MCI | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 1 | 1 |
Ranking | Drafter 1 | Type of Drafter 1 | Drafter 2 | Type of Drafter 2 | Predictive Value |
---|---|---|---|---|---|
1 | Institute of Environmental and Health Related Product Safety, Chinese Center for Disease Control and Prevention | Government department | Shanghai Municipal Center for Disease Control and Prevention | Government department | 0.9032 |
2 | Institute of Environmental and Health Related Product Safety, Chinese Center for Disease Control and Prevention | Government department | Health Supervision Institute of Huai ’an City, Jiangsu Province | Government department | 0.8852 |
3 | Institute of Environmental and Health Related Product Safety, Chinese Center for Disease Control and Prevention | Government department | Fudan University | Research Institute | 0.8523 |
4 | Jiangsu Provincial Center for Disease Control and Prevention | Government department | Wuhan Health and Epidemic Prevention Station | Government department | 0.8482 |
5 | Institute of Environmental and Health Related Product Safety, Chinese Center for Disease Control and Prevention | Government department | Institute of Environmental Health Monitoring, Chinese Academy of Preventive Medicine | Government department | 0.8239 |
6 | Institute of Environmental and Health Related Product Safety, Chinese Center for Disease Control and Prevention | Government department | Institute of Environmental Hygiene and Health Engineering, Chinese Academy of Preventive Medicine | Government department | 0.7813 |
7 | Supervision Institute of Beijing Municipal Health Commission | Government departments | Qingdao Institute of Environmental Health Science | Research Institute | 0.7803 |
8 | Wuhan Health and Epidemic Prevention Station | Government departments | Nantong University | Research Institute | 0.7614 |
9 | Sichuan Provincial Center for Disease Control and Prevention | Government departments | Liaoning Provincial Center for Disease Control and Prevention | Government department | 0.7513 |
10 | National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention | Government departments | Zhejiang Provincial Center for Disease Control and Prevention | Government department | 0.6721 |
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Yi, F.; Zhang, X.; Zhang, J.; Wei, Y. An Integrated Method for Cooperation Prediction in Complex Standard Networks. Systems 2024, 12, 257. https://doi.org/10.3390/systems12070257
Yi F, Zhang X, Zhang J, Wei Y. An Integrated Method for Cooperation Prediction in Complex Standard Networks. Systems. 2024; 12(7):257. https://doi.org/10.3390/systems12070257
Chicago/Turabian StyleYi, Feiteng, Xuan Zhang, Jiali Zhang, and Yongchang Wei. 2024. "An Integrated Method for Cooperation Prediction in Complex Standard Networks" Systems 12, no. 7: 257. https://doi.org/10.3390/systems12070257
APA StyleYi, F., Zhang, X., Zhang, J., & Wei, Y. (2024). An Integrated Method for Cooperation Prediction in Complex Standard Networks. Systems, 12(7), 257. https://doi.org/10.3390/systems12070257