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Diffusion networks: dynamic aspects of network theory and practice

https://doi.org/10.31249/poln/2021.04.05

Abstract

Comparative analysis revealed insufficient representation of the theories of network dynamics in comparison with the theories of network statics, with the exception of the “strategic relational theory of network dynamics” by K. Hay and D. Richards, the actor-network theory of B. Latour and the theory of stochastic processes, on the basis of which most models of network dynamics are built. In comparison with the relational and stochastic approaches, the diffusion direction describing the corresponding type of networks is less represented in the publications. The object of this study is diffusion networks, considered as a communicative element of the process of policy diffusion, that is, a channel for policy dissemination from one policy subject to another. The subject of the study is the political practices of cognitive control in diffusion networks. The methodological basis was the concept of the dynamics of diffusion networks, which allows us to describe the effects of “cognitive limitations” that arise in the Internet. In the development of this topic, it is planned to continue the research in the direction of identifying technologies of cognitive control in diffusion networks based on the manipulation of the cognitive abilities of participants in network relations. The empirical part of the study is aimed at testing the theoretical provisions of the concept of the dynamics of diffusion networks on the example of the practices of network control in the form of political cognitive censorship in the course of digital campaigns. To substantiate the conclusions, we use the big data analysis carried out by monitoring the online network space using the resources of the “Medialogia” and “YouScan” systems. The result was the conceptualization of the concept of “cognitive network control” in relation to diffusion networks, the description of the main dynamic indicator - the speed of dissemination of political information in network communities, and the identification of technologies for cognitive strategic influence in digital practices.

About the Author

T. A. Podshibyakina
Southern federal university
Russian Federation

Rostov-on-Don



References

1. Bartels K., Turnbull N. Relational public administration: a synthesis and heuristic classification of relational approaches. Public management review. 2020, Vol. 22, N 9, P. 1324-1346. DOI: 10.1080/14719037.2019.1632921

2. Boehmke F.J. Brockway M., Desmarais B.A., Harden J.J., LaCombe S., Linder F., Wallach H. SPID: A new database for inferring public policy innovativeness and diffusion networks. Policy studies journal. 2020, Vol. 48, N 2, P. 517-545. DOI: 10.1111/psj.12357

3. Bennett W.L., Segerberg A. The logic of connective action: digital media and the personalization of contentious politics. Information, communication & society. 2012, Vol. 15, N 5, P. 739-768. DOI: 10.1080/1369118X.2012.670661

4. Bond R., Fariss C., Jones J., Kramer A., Marlow C., Settle J., Fowler J. A 61-million-person experiment in social influence and political mobilization. Nature. 2012, Vol. 489, P. 295-298. DOI: 10.1038/nature11421

5. Borgatti S.P., Mehra A., Brass D.J., Labianca G. Network analysis in the social Sciences.Science. 2009, Vol. 323, N 5916, P. 892-895. DOI: 10.1126/science.1165821

6. Boushey G. Punctuated equilibrium theory and the diffusion of innovations. Policy studies journal. 2012, Vol. 40, N 1, P. 127-146. DOI: 10.1111/j.1541-0072.2011.00437.x

7. Cannarella J., Spechler J.A. Epidemiological modeling of online social network dynamics. arXiv preprint arXiv:1401.4208. 2014. Mode of access: https://arxiv.org/abs/1401.4208 (accessed: 02.07.2021).

8. Castells M. The rise of the network society. Oxford: Blackwell publishers, 1996, 656 p.

9. Desmarais B.A., Harden J.J., Boehmke F.J. Persistent policy pathways: inferring diffusion networks in the American states. American political science review. 2015, Vol. 109, N 2, P. 392-406. DOI: 10.1017/s0003055415000040

10. Gilardi F., Shipan C.R., Wüst B. Policy diffusion: the issue-definition stage. University of Zurich and University of Michigan, 2018. Mode of access: https://fabriziogilardi.org/resources/papers/policy-diffusion-issue-definition.pdf (accessed: 28.07.2020).

11. Gilardi F., Shipan C.R., Wüest B. The diffusion of policy frames: evidence from a structural topic model. American political science association. Annual meeting, Philadelphia, 1 September 2016-4 September 2016. 2016. Mode of access: https://www.zora.uzh.ch/id/eprint/143864/(accessed: 28.08.2020).

12. Greenan C.C. Diffusion of innovations in dynamic networks. Journal of the Royal statistical society. Series A (statistics in society). 2015, Vol. 178, N 1, P. 147-166. DOI: 10.1111/rssa.12054

13. Hay C., Richards D. The tangled web of Westminster and Whitehall: the discourse, strategy and practice of networking within the British core executive. Public administration. 2000, Vol. 78, N 1, P. 1-28. DOI: 10.1111/1467-9299.00190

14. Hodas N., Lerman K. The simple rules of social contagion. Scientific reports. 2014, Vol. 4, N 4343. DOI: 10.1038/srep04343

15. Holland P.W., Leinhardt S. A dynamic model for social networks. Journal of mathematical sociology. 1977, Vol. 5, N 1, P. 5-20. DOI: 10.1080/0022250x.1977.9989862

16. Jack S.L. The role, use and activation of strong and weak network ties: a qualitative analysis. Journal of management studies. 2005, Vol. 42, N 6, P. 1233-1259. DOI: 10.1111/j.1467-6486.2005.00540.x

17. Jiang C., Chen Y., Liu K.J.R. Evolutionary dynamics of information diffusion over social networks. IEEE transactions on signal processing. 2014, Vol. 62, N 17, P. 4573-4586. DOI: 10.1109/tsp.2014.2339799

18. Kim M., Newth D., Christen P. Modeling dynamics of diffusion across heterogeneous social networks: news diffusion in social media. Entropy. 2013, Vol. 15, N 10, P. 4215-4242. DOI: 10.3390/e15104215

19. Lane D.C. Should system dynamics be described as a ‘hard' or ‘deterministic' systems approach? Systems research and behavioral science: the official journal of the international federation for systems research. 2000, Vol. 17, N 1, P. 3-22. :1%3C3::aid-sres344%3E3.0.co;2-7. DOI: 10.1002/(sici)1099-1743(200001/02)17

20. Latour B. Reassembling the social: an introduction to actornetwork-theory. Clarendon lectures in management studies. New York: Oxford university press, 2005, 301 p.

21. Lerman K. Information is not a virus, and other consequences of human cognitive limits. Future Internet. 2016, Vol. 8, N 2, P. 21. DOI: 10.3390/fi8020021 EDN: YDPAEM

22. Luo S., Du Y., Liu P., Xuan Z., Wang Y. A study on coevolutionary dynamics of knowledge diffusion and social network structure. Expert systems with applications. 2015, Vol. 42, N 7, P. 3619-3633. DOI: 10.1016/j.eswa.2014.12.038

23. Lupeng Z., Chen W. How do innovation network structures affect knowledge sharing? A simulation analysis of complex networks. Complexity. 2021, Vol. 21, P. 17. DOI: 10.1155/2021/5107630

24. Maggetti M. The rewards of cooperation: the effects of membership in European regulatory networks. European journal of political research. 2014, Vol. 53, N 3, P. 480-499. DOI: 10.1111/1475-6765.12042

25. Netelenbos B. Bringing back Max Weber into network governance research. Critical policy studies. 2020, Vol. 14, N 1, P. 67-85. DOI: 10.1080/19460171.2018.1523738

26. Peck J., Theodore N. Fast policy: experimental statecraft at the thresholds of neoliberalism. U.S.: University of Minnesota press, 2015. Mode of access: http://ebookcentral.proquest.com/lib/UWSAU/detail.action?docID=205050 (accessed: 28.08.2020).

27. Plesner U. An actor-network perspective on changing work practices: Communication technologies as actants in newswork. Journalism. 2009, Vol. 10, N 5, P. 604-626. DOI: 10.1177/1464884909106535

28. Potseluev S.P., Konstantinov M.S., Podshibyakina T.A. Stratecies of cognitive political censorship as an effect "new media". Dilemas contemporaneous: educacion, politica y valores. 2020, Vol. 7, N 2, P. 91. DOI: 10.46377/dilemas.v33i1.2186 EDN: UXGNCZ

29. Rodriguez M.G., Balduzzi D., Schölkopf B. Uncovering the temporal dynamics of diffusion networks. arXiv preprint arXiv:1105.0697. 2011. Mode of access: https://arxiv.org/abs/1105.0697 (accessed: 02.07.2021).

30. Schaefer, D.R., Marcum, C.S. Modeling network dynamics. In: The Oxford handbook of social networks. 2017, P. 254-287.

31. Shields R. Flow as a new paradigm. Space and culture. 1997, Vol. 1, N 1, P. 1-7. DOI: 10.1177/120633129700100101

32. Snijders T.A.B. Stochastic actor-oriented models for network dynamics. Annual review of statistics and its application. 2017, Vol. 4, P. 343-363. DOI: 10.1146/annurev-statistics-060116-054035

33. Suitor J.J., Wellman B., Morgan D.L. It's about time: how, why, and when networks change. Social networks. 1997, Vol. 19, N 1, P. 1-7. DOI: 10.1016/s0378-8733(96)00287-0

34. Valente T.W. Network models of the diffusion of innovations. Computational and mathematical organization theory. 1996, Vol. 2, N 2, P. 163-164. DOI: 10.1007/bf00240425

35. Ivanov D.V. New approach to assessment of social development. Sociological studies. 2021, N 1, P. 50–62. DOI: https://doi.org/10.31857/S013216250010462-1 (In Russ.)

36. Smorgunov L.V. Network theory of politics and management. In: Gaman-Golutvina O.V., Nikitin A.I. (eds). Modern political theory. Methodology. Moscow : Aspect Press, 2017, P. 233–261. (In Russ.)


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ISSN 1998-1775 (Print)