Dynamics of macroscopic diffusion across meta-populations with top-down and bottom-up approaches: A review

Minkyoung Kim; Soohwan Kim; Minkyoung Kim; Soohwan Kim

doi:10.3934/mbe.2022213

Mathematical Biosciences and Engineering

2022, Volume 19, Issue 5: 4610-4626. doi: 10.3934/mbe.2022213

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Dynamics of macroscopic diffusion across meta-populations with top-down and bottom-up approaches: A review

Minkyoung Kim ,
Soohwan Kim ^,

Department of Artificial Intelligence and Software Technology, Sunmoon University, South Korea

Received: 30 December 2021 Revised: 08 February 2022 Accepted: 28 February 2022 Published: 07 March 2022

Human interaction patterns on the Web over online social networks vary with the context of communication items (e.g., politics, economics, disasters, celebrities, and etc.), which leads to form unlimited time-evolving curves of information adoption as diffusion proceeds. Online communications often continue to navigate through heterogeneous social systems consisting of a wide range of online media such as social networking sites, blogs, and mainstream news. This makes it very challenging to uncover the underlying causal mechanisms of such macroscopic diffusion. In this respect, we review both top-down and bottom-up approaches to understand the underlying dynamics of an individual item's popularity growth across multiple meta-populations in a complementary way. For a case study, we use a dataset consisting of time-series adopters for over 60 news topics through different online communication channels on the Web. In order to find disparate patterns of macroscopic information propagation, we first generate and cluster the diffusion curves for each target meta-population and then estimate them with two different and complementary approaches in terms of the strength and directionality of influences across the meta-populations. In terms of the strength of influence, we find that synchronous global diffusion is not possible without very strong intra-influence on each population. In terms of the directionality of influence between populations, such concurrent propagation is likely brought by transitive relations among heterogeneous populations. When it comes to social context, controversial news topics in politics and human culture (e.g., political protests, multiculturalism failure) tend to trigger more synchronous than asynchronous diffusion patterns across different social media on the Web. We expect that this study can help to understand dynamics of macroscopic diffusion across complex systems in diverse application domains.
- macroscopic diffusion,
- meta-populations,
- synchronous diffusion,
- top-down approach,
- bottom-up approach
Citation: Minkyoung Kim, Soohwan Kim. Dynamics of macroscopic diffusion across meta-populations with top-down and bottom-up approaches: A review[J]. Mathematical Biosciences and Engineering, 2022, 19(5): 4610-4626. doi: 10.3934/mbe.2022213

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Abstract

Human interaction patterns on the Web over online social networks vary with the context of communication items (e.g., politics, economics, disasters, celebrities, and etc.), which leads to form unlimited time-evolving curves of information adoption as diffusion proceeds. Online communications often continue to navigate through heterogeneous social systems consisting of a wide range of online media such as social networking sites, blogs, and mainstream news. This makes it very challenging to uncover the underlying causal mechanisms of such macroscopic diffusion. In this respect, we review both top-down and bottom-up approaches to understand the underlying dynamics of an individual item's popularity growth across multiple meta-populations in a complementary way. For a case study, we use a dataset consisting of time-series adopters for over 60 news topics through different online communication channels on the Web. In order to find disparate patterns of macroscopic information propagation, we first generate and cluster the diffusion curves for each target meta-population and then estimate them with two different and complementary approaches in terms of the strength and directionality of influences across the meta-populations. In terms of the strength of influence, we find that synchronous global diffusion is not possible without very strong intra-influence on each population. In terms of the directionality of influence between populations, such concurrent propagation is likely brought by transitive relations among heterogeneous populations. When it comes to social context, controversial news topics in politics and human culture (e.g., political protests, multiculturalism failure) tend to trigger more synchronous than asynchronous diffusion patterns across different social media on the Web. We expect that this study can help to understand dynamics of macroscopic diffusion across complex systems in diverse application domains.

References

[1]	M. Kim, D. Paini, R. Jurdak, Real-world diffusion dynamics based on point process approaches: A review, Artific. Intell. Rev., 53 (2020), 321–350. https://doi.org/10.1007/s10462-018-9656-9 doi: 10.1007/s10462-018-9656-9
[2]	S. Gao, J. Ma, Z. Chen, Modeling and predicting retweeting dynamics on microblogging platforms, in Proc. 8th ACM Int'l Conf. Web Search Web Data Mining, (2015), 107–116. https://doi.org/10.1145/2684822.2685303
[3]	H. Shen, D. Wang, C. Song, A.-L. Barabási, Modeling and predicting popularity dynamics via reinforced poisson processes, in Proc. 28th AAAI Conf. Artific. Intell, (2014), 291–297.
[4]	D. Wang, C. Song, A.-L. Barabási, Quantifying long-term scientific impact, Science, 342 (2013), 127–132. https://doi.org/10.1126/science.1237825 doi: 10.1126/science.1237825
[5]	Q. Zhao, M. A. Erdogdu, H. Y. He, A. Rajaraman, J. Leskovec, Seismic: A self-exciting point process model for predicting tweet popularity, in Proc. ACM SIGKDD Int'l Conf. Knowl. Disc. Data Mining, (2015), 1513–1522. https://doi.org/10.1145/2783258.2783401
[6]	M. Kim, D. Newth, P. Christen, Modeling dynamics of diffusion across heterogeneous social networks: News diffusion in social media, Entropy, 15 (2013), 4215–4242. https://doi.org/10.3390/e15104215 doi: 10.3390/e15104215
[7]	M. Kim, L. Xie, P. Christen, Event diffusion patterns in social media, in Proc. 6th Int'l AAAI Conf. Weblogs Soc. Media, (2012), 178–185.
[8]	L. M. Aiello, D. Quercia, K. Zhou, M. Constantinides, S. Šćepanović, S. Joglekar, How epidemic psychology works on Twitter: Evolution of responses to the COVID-19 pandemic in the U.S., Humanit. Soc. Sci. Commun., 8 (2021), 1–15. https://doi.org/10.1057/s41599-021-00861-3 doi: 10.1057/s41599-021-00861-3
[9]	M. Kim, D. Paini, R. Jurdak, Modeling stochastic processes in disease spread across a heterogeneous social system, The Proc. Nat. Acad. Sci., 116 (2019), 401–406. https://doi.org/10.1073/pnas.1801429116 doi: 10.1073/pnas.1801429116
[10]	R. Crane, D. Sornette, Robust dynamic classes revealed by measuring the response function of a social system, The Proc. Nat. Acad. Sci., 105 (2008), 15649–15653. https://doi.org/10.1073/pnas.0803685105 doi: 10.1073/pnas.0803685105
[11]	M. Kim, Understanding time-evolving citation dynamics across fields of sciences, Appl. Sci., 10 (2020), 5846. https://doi.org/10.3390/app10175846 doi: 10.3390/app10175846
[12]	H. A. Simon, The Sciences of the Artificial, Reissue of The Third Edition With A New Introduction by John Laird, The MIT Press, 2019.
[13]	M. Kim, D. Newth, P. Christen, Modeling dynamics of meta-populations with a probabilistic approach: Global diffusion in social media, in Proc. 22nd ACM Int. Conf. Info. Knowl. Manag., (2013), 489–498. https://doi.org/10.1145/2505515.2505583
[14]	M. Kim, Dynamics of Information Diffusion, Ph.D. thesis, The Australian National University, 2015.
[15]	M. Kim, R. Jurdak, Heterogeneous social signals capturing real-world diffusion processes, in Proc. 2nd Int. Wksp. Soc. Sens., (2017), 95. https://doi.org/10.1145/3055601.3055617
[16]	K. Zhang, M. Kim, R. Jurdak, D. Paini, Predictability of irregular human mobility, arXiv: 1709.08486. https://doi.org/10.48550/arXiv.1709.08486
[17]	E. Rogers, Diffusion of Innovations, Free Press of Glencoe, New York, 1962.
[18]	M. Kim, D. A. McFarland, J. Leskovec, Modeling affinity based popularity dynamics, in Proc. ACM Conf. Info. Knowl. Manag., (2017), 477–486. https://doi.org/10.1145/3132847.3132923
[19]	S. Goel, D. Watts, D. Goldstein, The structure of online diffusion networks, in Proc. 13th ACM Conf. Electr. Comm., (2012), 623–638. https://doi.org/10.1145/2229012.2229058
[20]	J. Leskovec, A. Singh, J. Kleinberg, Patterns of influence in a recommendation network, in Proc. Pacific-Asia Conf. Knowl. Disc. Data Mining, (2006), 380–389. https://doi.org/10.1007/11731139_44
[21]	R. Milo, S. Itzkovitz, N. Kashtan, R. Levitt, S. Shen-Orr, I. Ayzenshtat, et al., Superfamilies of evolved and designed networks, Science, 303 (2004), 1538–1542. https://doi.org/10.1126/science.1089167 doi: 10.1126/science.1089167
[22]	C. M. Schneider, V. Belik, T. Couronné, Z. Smoreda, M. C. González, Unravelling daily human mobility motifs, J. Roy. Soc. J. Royal Soc. Interf., 10 (2013), 20130246. https://doi.org/10.1098/rsif.2013.0246 doi: 10.1098/rsif.2013.0246
[23]	F. M. Bass, Comments on "a new product growth for model consumer durables: the Bass model", Manag. Sci., 50 (2004), 1833–1840. https://doi.org/10.1287/mnsc.1040.0300 doi: 10.1287/mnsc.1040.0300
[24]	M. E. Newman, Networks: An Introduction, Oxford University Press, 2010. https://doi.org/10.1093/acprof:oso/9780199206650.001.0001
[25]	V. Kumar, T. V. Krishnan, Multinational diffusion models: An alternative framework, Mktg. Sci., 21 (2002), 318–330.
[26]	W. Putsis Jr, S. Balasubramanian, E. Kaplan, S. Sen, Mixing behavior in cross-country diffusion, Mktg. Sci., 16 (1997), 354–369. https://doi.org/10.1287/mksc.16.4.354 doi: 10.1287/mksc.16.4.354
[27]	M. Kuperman, G. Abramson, Small world effect in an epidemiological model, Phys. Rev. Lett., 86 (2001), 2909–2912. https://doi.org/10.1103/PhysRevLett.86.2909 doi: 10.1103/PhysRevLett.86.2909
[28]	M. Luu, E. Lim, T. Hoang, F. Chua, Modeling diffusion in social networks using network properties, in Proc. 6th Int'l AAAI Conf. Weblogs Soc. Media (2012), 218–225.
[29]	M. Schilling, C. Phelps, Interfirm collaboration networks: The impact of large-scale network structure on firm innovation, Manag. Sci., 53 (2007), 1113–1126.
[30]	D. J. Daley, D. Vere-Jones, An Introduction to the Theory of Point Processes – Volume II: General Theory and Structure, Springer, 2008.
[31]	A. G. Hawkes, Spectra of some self-exciting and mutually exciting point processes, Biometrika, 58 (1971), 83–90. https://doi.org/10.2307/2334319 doi: 10.2307/2334319
[32]	J. Møller, J. G. Rasmussen, Perfect simulation of hawkes processes, Adv. Appl. Probab., 37 (2005), 629–646.
[33]	E. Cinlar, Introduction to Stochastic Processes, Courier Corporation, 2013.
[34]	T. M. Cover, J. A. Thomas, Elements of Information Theory, 2nd Edition, John Wiley & Sons, 2012.
[35]	R. Ghosh, T. Surachawala, K. Lerman, Entropy-based classification of 'retweeting' activity on Twitter, arXiv: 1106.0346 (2011).
[36]	C. Song, Z. Qu, N. Blumm, A.-L. Barabási, Limits of predictability in human mobility, Science, 327 (2010), 1018–1021. https://doi.org/10.1126/science.1177170 doi: 10.1126/science.1177170
[37]	R. Sinatra, M. Szell, Entropy and the predictability of online life, Entropy, 16 (2014), 543–556. https://doi.org/10.3390/e16010543 doi: 10.3390/e16010543
[38]	C. Wang, B. A. Huberman, How random are online social interactions?, Sci. Rep., 2 (2012). https://doi.org/10.1038/srep00633
[39]	G. Ver Steeg, A. Galstyan, Information transfer in social media, in Proc. 21st Int'l Conf. World Wide Web, (2012), 509–518. https://doi.org/10.1145/2187836.2187906
[40]	G. Ver Steeg, A. Galstyan, Information-theoretic measures of influence based on content dynamics, in Proc. 6th ACM Int'l Conf. Web Search Web Data Mining (2013), 3–12. https://doi.org/10.1145/2433396.2433400
[41]	M. Kim, D. Newth, P. Christen, Macro-level information transfer in social media: Reflections of crowd phenomena, Neurocomputing, 172 (2016), 84–99. https://doi.org/10.1016/j.neucom.2014.12.107 doi: 10.1016/j.neucom.2014.12.107
[42]	T. Schreiber, Measuring information transfer, Phys. Rev. Lett. 85 (2000), 461–464. https://doi.org/10.1103/PhysRevLett.85.461 doi: 10.1103/PhysRevLett.85.461
[43]	D. J. MacKay, Information Theory, Inference, and Learning Algorithms, Cambridge University Press, 2003.
[44]	J. A. Hartigan, M. A. Wong, Algorithm AS 136: A k-means clustering algorithm, J. Roy. Stat. Soc. Ser. C (Appl. Stat.), 28 (1979), 100–108. https://doi.org/10.2307/2346830 doi: 10.2307/2346830

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