A framework for continuum modeling of opinion dynamics on a network based on the probability of connections

Gianluca Favre , Gaspard Jankowiak , Sara Merino-Aceituno , Lara Trussardi

Networks and Heterogeneous Media ›› 2026, Vol. 21 ›› Issue (2) : 594 -631.

PDF (1945KB)
Networks and Heterogeneous Media ›› 2026, Vol. 21 ›› Issue (2) :594 -631. DOI: 10.3934/nhm.2026027
Research article
research-article
A framework for continuum modeling of opinion dynamics on a network based on the probability of connections
Author information +
History +
PDF (1945KB)

Abstract

We propose a modeling framework to develop a continuum description of opinion dynamics on networks as an alternative to other models, like the ones based on graphons. In a nutshell, the continuum model that we propose aimed to approximate the distribution of opinions and the probability that two given opinions are connected. To illustrate our framework, we focused on a simple model of consensus dynamics on a network and derived a continuum description using techniques inspired by mean-field limits. We also discussed the limitations of this approach and suggested extensions to account for dynamic networks with evolving connections, stochastic effects, and directional interactions.

Keywords

opinion formation / network mediated dynamics / systems of nonlinear conservation laws / large population limits

Cite this article

Download citation ▾
Gianluca Favre, Gaspard Jankowiak, Sara Merino-Aceituno, Lara Trussardi. A framework for continuum modeling of opinion dynamics on a network based on the probability of connections. Networks and Heterogeneous Media, 2026, 21 (2) : 594-631 DOI:10.3934/nhm.2026027

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

M. A. Gkogkas, C. Kuehn, C. Xu, Continuum limits for adaptive network dynamics, Commun. Math. Sci., 21(2023), 83-106. https://dx.doi.org/10.4310/CMS.2023.v21.n1.a4

[2]

M. A. Gkogkas, C. Kuehn, C. Xu, Mean field limits of co-evolutionary heterogeneous networks, Eur. J. Appl. Math., 2025, 1-44. https://doi.org/10.1017/S0956792524000858

[3]

C. Kuehn, C. Pulido, Mean-field limits for stochastic interacting particles on digraph measures, J. of Differ. Equations, 456(2026), 114054. https://doi.org/10.1016/j.jde.2025.114054

[4]

C. Kuehn, Network dynamics on graphops, New J. Phys., 22(2020), 053030. https://doi.org/10.1088/1367-2630/ab83d4

[5]

P. Degond, F. Delebecque, D. Peurichard, Continuum model for linked fibers with alignment interactions, Math. Models Methods Appl. Sci., 26(2016), 269-318. https://doi.org/10.1142/S0218202516400030

[6]

J. Barré, J. A. Carrillo, P. Degond, D. Peurichard, E. Zatorska, Particle interactions mediated by dynamical networks: assessment of macroscopic descriptions, J. Nonlinear Sci., 28(2018), 235-268. https://doi.org/10.1007/s00332-017-9408-z

[7]

J. Barré, P. Degond, E. Zatorska, Kinetic theory of particle interactions mediated by dynamical networks, Multiscale Model. Simul., 15(2017), 1294-1323. https://doi.org/10.1137/16M1085310

[8]

J. Barré, P. Dobson, M. Ottobre, E. Zatorska, Fast non-mean-field networks: Uniform in time averaging, SIAM J. Math. Anal., 53(2021), 937-972. https://doi.org/10.1137/20M1328646

[9]

N. Ayi, N. P. Duteil, Large-Population Limits of Non-exchangeable Particle Systems, In: Carrillo, J. A., Tadmor, E. (eds) Active Particles, Volume 4. Modeling and Simulation in Science, Engineering and Technology. Birkhäuser, Cham, 2024, 79-133. https://doi.org/10.1007/978-3-031-73423-6 3

[10]

P. E. Jabin, D. Poyato, J. Soler, Mean-field limit of non-exchangeable systems, Comm. Pure Appl. Math., 78(2025), 651-741. https://doi.org/10.1002/cpa.22235

[11]

T. Paul, E. Trélat, From microscopic to macroscopic scale equations: Mean field, hydrodynamic and graph limits, Preprint: 2209.08832. https://doi.org/10.48550/arXiv.2209.08832

[12]

N. Ayi, N. Pouradier Duteil, Mean-field and graph limits for collective dynamics models with time-varying weights, J. Differ. Equations, 299(2021), 65-110. https://doi.org/10.1016/j.jde.2021.07.010

[13]

R. P. Abelson, Mathematical models in social psychology, Adv. Exp. Soc. Psychol., 3(1967), 1-54. https://doi.org/10.1016/S0065-2601(08)60341-X

[14]

R. Hegselmann, U. Krause, Opinion dynamics and bounded confidence models, analysis, and simulation, J. Artif. Soc. Soc. Simul., 5, 2002. Available from: https://www.jasss.org/5/3/2.html.

[15]

A. Lancichinetti, S. Fortunato, F. Radicchi, Benchmark graphs for testing community detection algorithms, Phys. Rev. E 78(2008), 046110. https://doi.org/10.1103/PhysRevE.78.046110

[16]

R. Abelson, Mathematical models of the distribution of attitudes under controversy, In N. Fredericksen, H. Gullicksen (eds), Contributions to Mathematical Psychology, Holt, Rinehart & Winston, New York, 1964.

[17]

J. R. P. French, A formal theory of social power, Psychol. Rev., 3(1956), 181-194. https://doi.org/10.1037/h0046123

[18]

F. Harary, A criterion for unanimity in French’s theory of social power, Studies in Social Power, In D. Cartwright (Ed.), Univer. Michigan, 1959, 168-182.

[19]

R. L. Berger, A necessary and sufficient condition for reaching a consensus using DeGroot’s method, J. Am. Stat. Assoc., 76(1981), 415-418. https://doi.org/10.1080/01621459.1981.10477662

[20]

M. H. Degroot, Reaching a consensus, J. Am. Stat. Assoc., 69(1974), 118-121. https://doi.org/10.2307/2285509

[21]

K. Lehrer, Social consensus and rational agnoiology, Synthese, 31(1975), 141-160. https://doi.org/10.1007/BF00869475

[22]

L. Shi, W. Li, M. Shi, K. Shi, Y. Cheng, Opinion polarization over signed social networks with quasi-structural balance, IEEE Trans. Autom. Control, 68(2023), 6867-6874. https://doi.org/10.1109/TAC.2023.3250090

[23]

A. Flache, M. Mäs, T. Feliciani, E. Chattoe-Brown, G. Deffuant, S. Huet, et al., Models of social influence: Towards the next frontiers, J. Artif. Soc. Soc. Simul., 20, 2017. https://doi.org/10.18564/jasss.3521

[24]

N. E. Friedkin, E. C. Johnsen, Social influence and opinions, J. Math. Sociol., 15(1990), 193-206. https://doi.org/10.1080/0022250X.1990.9990069

[25]

N. E. Friedkin, E. C. Johnsen, Social Influence Network Theory: A Sociological Examination of Small Group Dynamics, Cambridge University Press, 2011. https://doi.org/10.1017/CBO9780511976735

[26]

K. Sznajd-Weron, J. Sznajd, Opinion evolution in closed community, Int. J. Mod. Phys. C, 11(2000), 1157-1165. https://doi.org/10.1142/S0129183100000936

[27]

F. Slanina, H. Lavicka, Analytical results for the Sznajd model of opinion formation, Eur. Phys. J. B, 35(2003), 279-288. https://doi.org/10.1140/epjb/e2003-00278-0

[28]

F. Cucker, S. Smale, Emergent behavior in flocks, IEEE Trans. Autom. Control, 52(2007), 852-862. https://doi.org/10.1109/TAC.2007.895842

[29]

F. Cucker, S. Smale, On the mathematics of emergence, Jpn. J. Math., 2(2007), 197-227. https://doi.org/10.1007/s11537-007-0647-x

[30]

G. Deffuant, D. Neau, F. Amblard, G. Weisbuch, Mixing beliefs among interacting agents, Adv. Complex Syst., 3(2000), 87-98. https://doi.org/10.1142/S0219525900000078

[31]

Z. Ding, X. Chen, Y. Dong, F. Herrera, Consensus reaching in social network DeGroot model: The roles of the self-confidence and node degree, Inf. Sci., 486(2019), 62-72. https://doi.org/10.1016/j.ins.2019.02.028

[32]

Y. Dong, Z. Ding, L. Martínez, F. Herrera, Managing consensus based on leadership in opinion dynamics, Inf. Sci., 397(2017), 187-205. https://doi.org/10.1016/j.ins.2017.02.052

[33]

P. E. Jabin, S. Motsch, Clustering and asymptotic behavior in opinion formation, J. Differ. Equations, 257(2014), 4165-4187. https://doi.org/10.1016/j.jde.2014.08.005

[34]

Y. Tian, P. Jia, A. Mirtabatabaei, L. Wang, N. E. Friedkin, F. Bullo, Social power evolution in influence networks with stubborn individuals, IEEE Trans. Auto. Control, 67(2021), 574-588. https://doi.org/10.1109/TAC.2021.3052485

[35]

G. Braghini, F. Salvarani, Effects of hidden opinion manipulation in microblogging platforms, Adv. Complex Syst., 24(2021), 2150009. https://doi.org/10.1142/S0219525921500090

[36]

H. Z. Brooks, M. A. Porter, A model for the influence of media on the ideology of content in online social networks, Phys. Rev. Res., 2(2020), 023041. https://doi.org/10.1103/PhysRevResearch.2.023041

[37]

A. Nugent, S. N. Gomes, M. T. Wolfram, Steering opinion dynamics through control of social networks, Chaos, 34(2024), 073109. https://doi.org/10.1063/5.0211026

[38]

M. Burger, Kinetic equations for processes on co-evolving networks, Kinet. Relat. Models, 15(2022), 187-212. https://doi.org/10.3934/krm.2021051

[39]

S. Fagioli, G. Favre, Opinion formation on evolving network: the DPA method applied to a nonlocal cross-diffusion PDE-ODE system, Eur. J. Appl. Math., 35(2024), 748-775. https://doi.org/10.1017/S0956792524000093

[40]

A. Nugent, S. N. Gomes, M. T. Wolfram, On evolving network models and their influence on opinion formation, Phys. D, 456(2023), 133914. https://doi.org/10.1016/j.physd.2023.133914

[41]

R. Berner, T. Gross, C. Kuehn, J. Kurths, S. Yanchuk, Adaptive dynamical networks, Phys. Rep., 1031(2023), 1-59. https://doi.org/10.1016/j.physrep.2023.08.001

[42]

J. Sawicki, R. Berner, S. A. M. Loos, M. Anvari, R. Bader, W. Barfuss, et al., Perspectives on adaptive dynamical systems, Chaos, 33(2023), 071501. https://doi.org/10.1063/5.0147231

[43]

C. Kuehn, C. Soresina, Cross-diffusion induced instability on networks, J. Complex Networks, 12(2024), cnad052. https://doi.org/10.1093/comnet/cnad052

[44]

S. Motsch, E. Tadmor, Heterophilious dynamics enhances consensus, SIAM Rev., 56(2014), 577-621. https://doi.org/10.1137/120901866

[45]

S. Fagioli, E. Radici, Opinion formation systems via deterministic particles approximation, Kinet. Relat. Models, 14(2021), 45-76. https://doi.org/10.3934/krm.2020048

[46]

B. M. Stokes, S. E. Jackson, P. Garnett, J. Luo, Extremism, segregation and oscillatory states emerge through collective opinion dynamics in a novel agent-based model, J. Math. Sociol., 48(2024), 42-80. https://doi.org/10.1080/0022250X.2022.2124246

[47]

G. Albi, L. Pareschi, M. Zanella, On the optimal control of opinion dynamics on evolving networks, In: L. Bociu, J. A. Désidéri, A. Habbal (eds), System Modeling and Optimization. CSMO 2015. IFIP Advances in Information and Communication Technology, vol 494. Springer, Cham, 2016. Available from: https://doi.org/10.1007/978-3-319-55795-34.

[48]

M. Burger, Network structured kinetic models of social interactions, Vietnam J. Math., 49(2021), 937-956. https://doi.org/10.1007/s10013-021-00505-8

[49]

N. Loy, M. Raviola, A. Tosin, Opinion polarization in social networks, Philos. Trans. A Math. Phys. Eng. Sci., 380(2022), 20210158. https://doi.org/10.1098/rsta.2021.0158

[50]

J. Dalege, M. Galesic, H. Olsson, Networks of beliefs: An integrative theory of individual- and social-level belief dynamics, Psychol. Rev., 132(2023), 253-290. https://doi.org/10.1037/rev0000494

[51]

S. Rosenstock, J. Bruner, C. O’Connor, In epistemic networks, is less really more? Philos. Sci., 84(2017), 234-252. https://doi.org/10.1086/690717

[52]

K. J. Zollman, Social network structure and the achievement of consensus, Politics Philos. Econ., 1(2012), 26-44. https://doi.org/10.1177/1470594X11416766

[53]

F. Baumann, P. Lorenz-Spreen, I. M. Sokolov, M. Starnini, Modeling echo chambers and polarization dynamics in social networks, Phys. Rev. Lett., 124(2020), 048301. https://doi.org/10.1103/PhysRevLett.124.048301

[54]

B. Düring, J. Franceschi, M. T. Wolfram, M. Zanella, Breaking consensus in kinetic opinion formation models on graphons, J. Nonlinear Sci., 34(2024), 79. https://doi.org/10.1007/s00332-024-10060-4

[55]

B. Düring, P. Markowich, J. F. Pietschmann, M. T. Wolfram, Boltzmann and Fokker-Planck equations modelling opinion formation in the presence of strong leaders, Proc. A, 465(2009), 3687-3708. https://doi.org/10.1098/rspa.2009.0239

[56]

B. Düring, O. Wright, On a kinetic opinion formation model for pre-election polling, Philos. Trans. A Math. Phys. Eng. Sci., 380(2022), 20210154. https://doi.org/10.1098/rsta.2021.0154

[57]

P. Degond, J. G. Liu, S. Merino-Aceituno, T. Tardiveau, Continuum dynamics of the intention field under weakly cohesive social interaction, Math. Models Methods Appl. Sci., 27(2017), 159-182. https://doi.org/10.1142/S021820251740005X

[58]

G. Dimarco, B. Perthame, G. Toscani, M. Zanella, Kinetic models for epidemic dynamics with social heterogeneity, J. Math. Biol, 83(2021), 4. https://doi.org/10.1007/s00285-021-01630-1

[59]

G. Toscani, A. Tosin, M. Zanella, Opinion modeling on social media and marketing aspects, Phys. Rev. E, 98(2018), 022315. https://doi.org/10.1103/PhysRevE.98.022315

[60]

P. E. Jabin, D. Zhou, The mean-field limit of sparse networks of integrate and fire neurons, Anal. Non Linéaire, 43(2026), 273-343. https://doi.org/10.4171/AIHPC/157

[61]

A. Dubovskaya, S. C. Fennell, K. Burke, J. P. Gleeson, D. O’Kiely, Analysis of mean-field approximation for Deffuant opinion dynamics on networks, SIAM J. Appl. Math., 83(2023), 436-459. https://doi.org/10.1137/22M149976

[62]

M. A. Gkogkas, C. Kuehn, Graphop mean-field limits for kuramoto-type models, SIAM J. Appl. Dyn. Syst., 21(2022), 248-283. https://doi.org/10.1137/21M1391523

[63]

M. Nurisso, M. Raviola, A. Tosin, Network-based kinetic models: Emergence of a statistical description of the graph topology, Eur. J. Appl. Math., 2024(2024), 1-22. https://doi.org/10.1017/S0956792524000020

[64]

J. LaSalle, S. Lefschetz, Stability by Liapunov’s direct method, with applications Mathematics in Science and Engineering, Academic Press, New York-London, 4(1961), 3-134. Available from: https://www.sciencedirect.com/bookseries/mathematics-in-science-and-engineering/vol/4/suppl/C.

[65]

A. V. Proskurnikov, R. Tempo, A tutorial on modeling and analysis of dynamic social networks. Part II, Annu. Rev. Control, 45(2018), 166-190. https://doi.org/10.1016/j.arcontrol.2018.03.005

[66]

R. Carmona, Lectures on BSDEs, stochastic control, and stochastic differential games with financial applications, Financial Mathematics, SIAM, 2016. https://doi.org/10.1137/1.9781611974249

[67]

A. S. Sznitman, Topics in propagation of chaos, Ecole d’étéd de Probabilités de Saint-Flour XIX-1989, 1464(1991), 165-251. https://doi.org/10.1007/BFb0085169

[68]

L. P. Chaintron, A. Diez, Propagation of chaos: A review of models, methods and applications. I. Models and methods, Kinet. Relat. Models, 15(2022), 895-1015. https://doi.org/10.3934/krm.2022017

[69]

L. P. Chaintron, A. Diez, Propagation of chaos: A review of models, methods and applications. II. Applications, 2022. Preprint: 2106.14812. Available from: https://hal.science/hal-03585067v3.

[70]

R. J. LeVeque, Finite Volume Methods for Hyperbolic Problems, Cambridge University Press, 2002. https://doi.org/10.1017/CBO9780511791253

[71]

S. J. Sheather, M. C. Jones, A reliable data-based bandwidth selection method for kernel density estimation, J. R. Stat. Soc., 53(1991), 683-690. https://doi.org/10.1111/j.2517-6161.1991.tb01857.x

[72]

Y. C. Chen, A tutorial on kernel density estimation and recent advances, Biostatistics Epidemiol., 1(2017), 161-187. https://doi.org/10.1080/24709360.2017.1396742

[73]

R. Diestel, Graph theory, volume 173 Graduate Texts in Mathematics, Springer-Verlag, Berlin, third edition, 173, 2005. Available from: http://www.math.uni-hamburg.de/home/diestel/books/graph.theory/.

[74]

M. Besançon, T. Papamarkou, D. Anthoff, A. Arslan, S. Byrne, D. Lin, et al., Distributions.jl: Definition and modeling of probability distributions in the JuliaStats ecosystem, J. Stat. Software, 98(2021), 1-30. https://doi.org/10.18637/jss.v098.i16

[75]

J. Fairbanks, M. Besançon, S. Simon, J. Hoffiman, N. Eubank, S. Karpinski, Juliagraphs/graphs.jl: An optimized graphs package for the Julia programming language, 2021. Available from: https://github.com/JuliaGraphs/Graphs.jl/.

[76]

S. Danisch, J. Krumbiegel, Makie.jl: Flexible high-performance data visualization for Julia, J. Open Source Software, 6(2021), 3349. https://doi.org/10.21105/joss.03349

[77]

D. Floros, LFRBenchmarkGraphs.jl, 2024. Available from: https://github.com/fcdimitr/LFRBenchmarkGraphs.jl.

[78]

G. Jankowiak, OpiForm. github.com/gjankowiak/OpiForm. Available from: https://github.com/gjankowiak/OpiForm.

[79]

L. Boudin, F. Salvarani, A kinetic approach to the study of opinion formation, ESAIM Math. Modell. Numer. Anal., 43(2009), 507-522. https://doi.org/10.1051/m2an/2009004

[80]

C. T. Nguyen, Echo chambers and epistemic bubbles, Episteme, 17(2020), 141-161. https://doi.org/10.1017/epi.2018.32

[81]

C. Turner, Online echo chambers, online epistemic bubbles, and open-mindedness, Episteme, 22(2025), 232-257. https://doi.org/10.1017/epi.2023.52

[82]

S. Gershgorin, Uber die Abgrenzung der Eigenwerte einer Matrix, Bull. Acad. Sci. URSS, 7(1931), 749-754.

PDF (1945KB)

23

Accesses

0

Citation

Detail

Sections
Recommended

/

〈 〉