Skip to main content

Advertisement

Springer Nature Link
Log in

Understanding and modeling the complex dynamics of the online social networks: a scalable conceptual approach

  • Review
  • Published:
Evolving Systems Aims and scope Submit manuscript

Abstract

The explosive growth of the online social networks gives rise to extensive qualitative and quantitative changes in human communication stemming from the direct and indirect online interaction among individuals, as well as between individuals and technological objects of the social web. In the online ecosystem self-organised communities emerge and evolve, while behavior, norms, trends, trust and collective activity patterns appear as macro-level properties originating from micro-level interactions among interconnected individuals. The study of online (and offline) social dynamical processes requires an approach capturing their evolutionary nature and their interplay with the external environment. A pertinent methodological framework is that of the Complex Adaptive Systems, whereby the network topology and the states of the nodes co-evolve owing to strong interaction, adaptation and learning. Social networks are characterized by complex, stoch-astic and non-equilibrium dynamics, and therefore their study and modeling call for an exploratory, piecemeal and hybrid approach bringing together concepts from the fields of complexity, network theory, dynamical systems, quantitative sociology and statistical physics. In this paper we consolidate methods from the aforementioned disciplines into a scalable conceptual approach, with a view to providing methodological and technical recommendations applicable to the study and modeling of dynamical phenomena occurring in online and offline social networks.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Notes

  1. A network percolates when there are enough links so that a global cluster emerges.

  2. Fig 4 obtained from the original paper by (De Domenico et al. 2013) under the terms of Creative Commons Attribution 3.0 License.

  3. The Figure was created through the use of the tool “Brownian Motion in 2D and the Fokker–Planck Equation” from the Wolfram Demonstrations Project http://demonstrations.wolfram.com/BrownianMotionIn2DAndTheFokkerPlanckEquation/ Contributed by: Alejandro Luque Estepa.

References

  • Albert R, Barabási AL (2002) Statistical mechanics of complex networks. Rev Modern Phy 74(1):47

    Article  MathSciNet  MATH  Google Scholar 

  • Amaral LA, Ottino JM (2004) Complex networks. Euro Phys J B Condens Matter Complex Syst 38(2):147–162

    Article  Google Scholar 

  • Anderson PW et al (1972) More is different. Science 177(4047):393–396

    Article  Google Scholar 

  • Angelov P (2012) Autonomous learning systems: from data streams to knowledge in real-time. Wiley, New York

    Book  Google Scholar 

  • Axelrod R (1997a) The dissemination of culture a model with local convergence and global polarization. J Confl Resolut 41(2):203–226

    Article  Google Scholar 

  • Axelrod R, Hamilton WD (1981) The evolution of cooperation. Science 211(4489):1390–1396

    Article  MathSciNet  MATH  Google Scholar 

  • Axelrod RM (1997b) The complexity of cooperation: agent-based models of competition and collaboration. Princeton University Press, Princeton

    Google Scholar 

  • Axelrod RM, Axelrod R, Cohen MD (2000) Harnessing complexity: organizational implications of a scientific frontier. Basic Books. ISBN:9780465005505

  • Ball P (2012) Why society is a complex matter. Springer, Berlin

    Book  Google Scholar 

  • Barrat A, Barthlemy M, Vespignani A (2008) Dynamical processes on complex networks. Cambridge University Press, Cambridge

    Book  MATH  Google Scholar 

  • Baruah RD, Angelov P (2012) Evolving social network analysis: a case study on mobile phone data. In: Evolving and adaptive intelligent systems (EAIS), 2012 IEEE Conference on IEEE, pp 114–120

  • Barzel B, Barabási AL (2013) Universality in network dynamics. Nat Phys 9(10):673–681

    Article  Google Scholar 

  • Bastian M, Heymann S, Jacomy M et al (2009) Gephi: an open source software for exploring and manipulating networks. ICWSM 8:361–362

    Google Scholar 

  • Battiston F, Nicosia V, Latora V (2014) Structural measures for multiplex networks. Phys Rev E 89(3):032804

    Article  Google Scholar 

  • Benchettara N, Kanawati R, Rouveirol C (2010) Supervised machine learning applied to link prediction in bipartite social networks. In: Advances in social networks analysis and mining (ASONAM), 2010 International conference on IEEE, pp 326–330

  • Bishop RC (2011) Metaphysical and epistemological issues in complex systems. Philos Complex Syst 10:105

    Article  Google Scholar 

  • Blume L, Durlauf S (2001) The interactions-based approach to socioeconomic behavior. In: Social dynamics. MIT press, Cambridge, pp 15–44

  • Boccaletti S, Latora V, Moreno Y, Chavez M, Hwang DU (2006) Complex networks: structure and dynamics. Phys Rep 424(4):175–308

    Article  MathSciNet  Google Scholar 

  • Boccaletti S, Bianconi G, Criado R, Del Genio C, Gómez-Gardeñes J, Romance M, Sendiña-Nadal I, Wang Z, Zanin M (2014) The structure and dynamics of multilayer networks. Phys Rep 544(1):1–122

    Article  MathSciNet  Google Scholar 

  • Bonabeau E (2002) Agent-based modeling: methods and techniques for simulating human systems. Proc Natl Acad Sci USA 99(Suppl 3):7280–7287

    Article  Google Scholar 

  • Bornholdt S, Röhl T (2003) Self-organized critical neural networks. Phys Rev E 67(6):066118

    Article  Google Scholar 

  • Bornholdt S, Rohlf T (2000) Topological evolution of dynamical networks: global criticality from local dynamics. Phys Rev Lett 84(26):6114–6117

    Article  Google Scholar 

  • Braha D, Bar-Yam Y (2009) Time-dependent complex networks: dynamic centrality, dynamic motifs, and cycles of social interactions. In: Gross T, Sayama H (eds) Adaptive Networks: Theory, Models and Applications. Springer, Berlin, pp 39–50

  • Budka M, Juszczyszyn K, Musial K, Musial A (2013) Molecular model of dynamic social network based on e-mail communication. Soc Network Anal Min 3(3):543–563

    Article  Google Scholar 

  • Caldarelli G, Garlaschelli D (2009) Self-organization and complex networks. In: Gross T, Sayama H (eds) Adaptive Networks: Theory, Models and Applications. Springer, Berlin, pp 107–135

  • Castellano C, Fortunato S, Loreto V (2009) Statistical physics of social dynamics. Rev Modern Phys 81(2):591

    Article  Google Scholar 

  • Cha M, Mislove A, Gummadi KP (2009) A measurement-driven analysis of information propagation in the flickr social network. In: Proceedings of the 18th international conference on World wide web, ACM, pp 721–730

  • Chaikin PM, Lubensky TC, Witten TA (2000) Principles of condensed matter physics, vol 1. Cambridge University Press, Cambridge

    Google Scholar 

  • Contractor NS, Wasserman S, Faust K (2006) Testing multitheoretical, multilevel hypotheses about organizational networks: an analytic framework and empirical example. Acad Manag Rev 31(3):681–703

    Article  Google Scholar 

  • Crutchfield JP (2008) Is anything ever new? Considering emergence. In: Emergence: Contemporary Readings in Philosophy and Science. MIT Press, Cambridge, MA, pp 269–286

  • De Domenico M, Solé-Ribalta A, Cozzo E, Kivelä M, Moreno Y, Porter MA, Gómez S, Arenas A (2013) Mathematical formulation of multilayer networks. Phys Rev X 3(4):041022

    Google Scholar 

  • De Sá HR, Prudêncio RBC (2011) Supervised link prediction in weighted networks. In: Neural networks (IJCNN), The 2011 International joint conference on IEEE, pp 2281–2288

  • Dorogovtsev SN, Mendes JF (2003) Evolution of networks: From biological nets to the Internet and WWW. Oxford University Press, Oxford

  • Dutta Baruah R, Angelov P (2013) Analysis of evolving social network: methods and results from cell phone dataset case study. Int J Soc Network Min 1(3–4):254–279

    Article  Google Scholar 

  • Ebeling W, Feistel R (1992) Theory of selforganization and evolution: the role off entropy, value and information1. J Non-Equilib Thermodyn 17(4):303–332

    MATH  Google Scholar 

  • Epstein JM (1999) Agent-based computational models and generative social science. Studies in Agent-Based Computational Modeling, Generative Social Science, pp 4–46

    Google Scholar 

  • Epstein JM, Axtell R (1996) Growing artificial societies: social science from the bottom up. The MIT Press, Cambridge

    Google Scholar 

  • Erdos P, Rényi A (1960) On the evolution of random graphs. Publ Math Inst Hungar Acad Sci 5:17–61

    MathSciNet  MATH  Google Scholar 

  • Feger H (1978) Konflikterleben und Konfliktverhalten: psychologische Untersuchungen zu alltäglichen Entscheidungen. Hans Huber, Bern

    Google Scholar 

  • Fuchs A (2013) Stochastic systems. In: Nonlinear dynamics in complex systems. Springer, Berlin, pp 105–129

  • Ganguly N, Deutsch A, Mukherjee A (2009) Dynamics on and of complex networks: applications to biology, computer science, and the social sciences. Springer, Berlin

    Book  MATH  Google Scholar 

  • Gomez S, Diaz-Guilera A, Gomez-Gardeñes J, Perez-Vicente CJ, Moreno Y, Arenas A (2013) Diffusion dynamics on multiplex networks. Phys Rev Lett 110(2):028701

    Article  Google Scholar 

  • Goodreau SM, Kitts JA, Morris M (2009) Birds of a feather, or friend of a friend? using exponential random graph models to investigate adolescent social networks. Demography 46(1):103–125

    Article  Google Scholar 

  • Gros PDC (2013) Chaos, bifurcations and diffusion.In: Complex and adaptive dynamical systems. Springer, Berlin, pp 41–84

  • Gross T, Blasius B (2008) Adaptive coevolutionary networks: a review. J R Soc Interface 5(20):259–271

    Article  Google Scholar 

  • Guille A, Hacid H (2012) A predictive model for the temporal dynamics of information diffusion in online social networks. In: Proceedings of the 21st international conference companion on World Wide Web, ACM, pp 1145–1152

  • Hanneman RA, Riddle M (2005) Introduction to social network methods. University of California, California

    Google Scholar 

  • Harms W (2011) Evolutionary games and the modeling of complex systems. Philos Complex Syst 10:163

    Article  Google Scholar 

  • Hasgall A (2013) Digital social networks as complex adaptive systems. VINE 43(1):78–95

    Article  Google Scholar 

  • Helbing D (1993) Boltzmann-like and boltzmann-fokker-planck equations as a foundation of behavioral models. Phys A Stat Mech Appl 196(4):546–573

    Article  MathSciNet  MATH  Google Scholar 

  • Helbing D (2010a) Boltzmann-like equations. In: Quantitative Sociodynamics. Springer, Berlin, pp 83–98

  • Helbing D (2010b) Evolutionary game theory. In: Quantitative Sociodynamics. Springer, Berlin, pp 247–274

  • Helbing D (2010c) Problems and terminology. In: Quantitative Sociodynamics. Springer, Berlin, pp 153–165

  • Helbing D (2010d) Quantitative sociodynamics. Springer, Berlin

    Book  MATH  Google Scholar 

  • Helbing D, Tilch B (1998) Generalized force model of traffic dynamics. Phys Rev E 58(1):133

    Article  Google Scholar 

  • Helbing D, Buzna L, Johansson A, Werner T (2005) Self-organized pedestrian crowd dynamics: experiments, simulations, and design solutions. Transp Sci 39(1):1–24

    Article  Google Scholar 

  • Hofkirchner W, Schafranek M (2011) General system theory. Philosophy of complexity, chaos, and non-linearity. Handb Philos Sci 10:177–194

    Google Scholar 

  • Holland JH (2006) Studying complex adaptive systems. J Syst Sci Complex 19(1):1–8

    Article  MathSciNet  MATH  Google Scholar 

  • Hooker C (2011) Conceptualising reduction, emergence and self-organisation in complex dynamical systems. Philos Complex Syst 10:195

    Article  MathSciNet  Google Scholar 

  • Ito J, Kaneko K (2003) Spontaneous structure formation in a network of dynamic elements. Phys Rev E 67(4):046226

    Article  Google Scholar 

  • Juszczyszyn K, Musial A, Musial K, Bródka P (2009) Molecular dynamics modelling of the temporal changes in complex networks. In: Evolutionary Computation (2009) CEC’09. IEEE congress on IEEE, pp 553–559

  • Juszczyszyn K, Budka M, Musial K (2011a) The dynamic structural patterns of social networks based on triad transitions. In: Advances in social networks analysis and mining (ASONAM), 2011 International conference on IEEE, pp 581–586

  • Juszczyszyn K, Musial K, Budka M (2011b) Link prediction based on subgraph evolution in dynamic social networks. In: Privacy, security, risk and trust (passat), 2011 IEEE third international conference on and 2011 IEEE third international conference on social computing (socialcom) IEEE, pp 27–34

  • Juszczyszyn K, Musial K, Budka M (2011c) On analysis of complex network dynamics—changes in local topology. In: The 17th ACM SIGKDD international conference on knowledge discovery and data mining (SNA-KDD), Bournemouth University, Fern Barrow, Poole, Dorset, BH12 5BB, UK

  • Kaplan D, Glass L (1995) Understanding nonlinear dynamics, vol 9. Springer, New York

    Book  MATH  Google Scholar 

  • Kivelä M, Arenas A, Barthelemy M, Gleeson JP, Moreno Y, Porter MA (2013) Multilayer networks. arXiv preprint arXiv:13097233

  • Leskovec J, Huttenlocher D, Kleinberg J (2010) Predicting positive and negative links in online social networks. In: Proceedings of the 19th international conference on World wide web, ACM, pp 641–650

  • Li S, Li J (2014) Web and social media dynamics, and evolutionary and adaptive branding: theories and a hybrid intelligent model. In: Proceedings of the 13th international conference on artificial intelligence, knowledge engineering and databases, Advances in Neural Networks, Fuzzy Systems and Artificial Intelligence, pp 106-111

  • Li X, Chen H (2013) Recommendation as link prediction in bipartite graphs: a graph kernel-based machine learning approach. Decis Support Syst 54(2):880–890

    Article  Google Scholar 

  • Lichtenwalter RN, Lussier JT, Chawla NV (2010) New perspectives and methods in link prediction. In: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, pp 243–252

  • Lozano S (2009) Dynamics of social complex networks: Some insights into recent research. In: Ganguly N, Deutsch A, Mukherjee A (eds) Dynamics On and of complex networks. Modeling and simulation in science, engineering and technology, Birkhuser, Boston, pp 133–143

  • Lukeman R, Li YX, Edelstein-Keshet L (2010) Inferring individual rules from collective behavior. Proc Natl Acad Sci 107(28):12,576–12,580

  • Lymperopoulos I, Lekakos G (2013) Analysis of social network dynamics with models from the theory of complex adaptive systems. Collaborative. In, Trusted and Privacy-Aware e/m-Services, Springer, Berlin, pp 124–140

    Google Scholar 

  • Lymperopoulos IN, Ioannou GD (2015) Online social contagion modeling through the dynamics of integrate-and-fire neurons. Inf Sci 320:26–61

    Article  Google Scholar 

  • Macy MW, Willer R (2002) From factors to actors: computational sociology and agent-based modeling. Ann Rev Sociol 28:143–166

    Article  Google Scholar 

  • Mainzer K (2007) Thinking in complexity: the computational dynamics of matter, mind, and mankind. Springer, Berlin

    MATH  Google Scholar 

  • McCabe C, Watson RA, Prichard J, Hall W (2011) The web as an adaptive network: coevolution of web behavior and web structure. In: Proceedings of the 3rd international web science conference, ACM

  • Miller JH, Page SE (2010) Complex adaptive systems: an introduction to computational models of social life: an introduction to computational models of social Life. Princeton University Press, Princeton

    MATH  Google Scholar 

  • Mislove A, Marcon M, Gummadi KP, Druschel P, Bhattacharjee B (2007) Measurement and analysis of online social networks. In: Proceedings of the 7th ACM SIGCOMM conference on Internet measurement, ACM, pp 29–42

  • Mislove A, Koppula HS, Gummadi KP, Druschel P, Bhattacharjee B (2008) Growth of the flickr social network. In: Proceedings of the first workshop on Online social networks, ACM, pp 25–30

  • Murray J (2004a) Continuous population models for single species. Mathematical biology, interdisciplinary applied mathematics, vol 17. Springer, New York, pp 1–43

  • Murray J (2004b) Models for interacting populations. Mathematical biology, interdisciplinary applied mathematics, vol 17. Springer, New York, pp 79–118

  • Newman M (2010) Networks: an introduction. Oxford University Press, Oxford

    Book  MATH  Google Scholar 

  • Newman M, Barabasi AL, Watts DJ (2011) The structure and dynamics of networks. Princeton University Press, Princeton

    Google Scholar 

  • Newman ME (2003) The structure and function of complex networks. SIAM Rev 45(2):167–256

    Article  MathSciNet  MATH  Google Scholar 

  • Nicolis G, Prigogine I (1977) Self-organization in nonequilibrium systems, vol 191977. Wiley, New York

  • Nikolic I, Dam K, Kasmire J (2013) Practice. In: Dam KH, Nikolic I, Lukszo Z (eds) Agent-based modelling of socio-technical systems, agent-based social systems, vol 9. Springer, Netherlands, pp 73–137

    Chapter  Google Scholar 

  • Palla G, Pollner P, Barabási AL, Vicsek T (2009) Social group dynamics in networks. In: Gross T, Sayama H (eds) Adaptive Networks: Theory, Models and Applications. Springer, Berlin, pp 11–38

  • Pastor-Satorras R, Vespignani A (2001) Epidemic spreading in scale-free networks. Phys Rev Lett 86(14):3200–3203

    Article  Google Scholar 

  • Phister PW Jr (2010) Cyberspace: the ultimate complex adaptive system. Tech. rep, DTIC Document

    Google Scholar 

  • Pikovsky A, Rosenblum M, Kurths J, Hilborn RC (2002) Synchronization: a universal concept in nonlinear science. Am J Phys 70(6):655–655

    Article  Google Scholar 

  • Rahmandad H, Sterman J (2008) Heterogeneity and network structure in the dynamics of diffusion: comparing agent-based and differential equation models. Manag Sci 54(5):998–1014

    Article  Google Scholar 

  • Reynolds CW (1987) Flocks, herds and schools: A distributed behavioral model. ACM SIGGRAPH Comput Gr ACM 21:25–34

    Article  Google Scholar 

  • Ricci A, Omicini A, Viroli M, Gardelli L, Oliva E (2007) Cognitive stigmergy: towards a framework based on agents and artifacts. Environments for multi-agent systems III. Springer, Berlin, pp 124–140

    Google Scholar 

  • Rogers EM, Medina UE, Rivera MA, Wiley CJ (2005) Complex adaptive systems and the diffusion of innovations. Innov J Public Sect Innov J 10(3):1–26

    Google Scholar 

  • Rohlf T (2008) Self-organization of heterogeneous topology and symmetry breaking in networks with adaptive thresholds and rewiring. EPL (Europhysics Letters) 84(1):10004

    Article  MathSciNet  Google Scholar 

  • Sayama H, Laramee C (2009) Generative network automata: a generalized framework for modeling adaptive network dynamics using graph rewritings. Adaptive networks. Springer, Berlin, pp 311–332

    Chapter  Google Scholar 

  • Sayama H, Pestov I, Schmidt J, Bush BJ, Wong C, Yamanoi J, Gross T (2013) Modeling complex systems with adaptive networks. Comput Math Appl

  • Scharfstein DS, Stein JC (1990) Herd behavior and investment. Am Econ Rev 80(3):465–479

  • Schelling TC (1972) A process of residential segregation: neighborhood tipping. Racial Discrim Econ Life 157:174

    Google Scholar 

  • Schelling TC (2006) Micromotives and macrobehavior. WW Norton, New York

    Google Scholar 

  • Schweitzer F, Garcia D (2010) An agent-based model of collective emotions in online communities. Euro Phys J B Condens Matter Complex Syst 77(4):533–545

    Article  Google Scholar 

  • Sen AK, Smith TE (1995) Gravity models of spatial interaction behavior. Springer, Heidelberg

    Book  Google Scholar 

  • Simon HA (1976) Administrative behavior, vol 3. Cambridge University Press, Cambridge

    Google Scholar 

  • Sinha S (2009) From network structure to dynamics and back again: relating dynamical stability and connection topology in biological complex systems. Dynamics on and of complex networks. Springer, Berlin, pp 3–17

    Chapter  Google Scholar 

  • Smith A (2006) An inquiry into the nature and causes of the wealth of nations. Echo Library, Teddington

    Google Scholar 

  • Strogatz SH (2001) Exploring complex networks. Nature 410(6825):268–276

    Article  Google Scholar 

  • Strogatz SH (2004) Sync: how order emerges from chaos in the universe, nature, and daily life. Hyperion Books. ISBN:9780786887217

  • Torii KU (2012) Two-dimensional spatial patterning in developmental systems. Trends Cell Biol 22(8):438–446

  • Traulsen A, Santos FC, Pacheco JM (2009) Evolutionary games in self-organizing populations. In: Gross T, Sayama H (eds) Adaptive Networks: Theory, Models and Applications. Springer, Berlin, pp 253–267

  • Verhulst PF (1838) Notice sur la loi que la population suit dans son accroissement. Correspondance mathématique et physique publiée par a. Quetelet 10:113–121

    Google Scholar 

  • Vespignani A (2011) Modelling dynamical processes in complex socio-technical systems. Nat Phys 8(1):32–39

    Article  Google Scholar 

  • Viswanath B, Mislove A, Cha M, Gummadi KP (2009) On the evolution of user interaction in facebook. In: Proceedings of the 2nd ACM workshop on Online social networks, ACM, pp 37–42

  • Von Bertalanffy L (1956) General system theory. Gen Syst 1:1–10

    Google Scholar 

  • Wang D, Pedreschi D, Song C, Giannotti F, Barabasi AL (2011) Human mobility, social ties, and link prediction. In: Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, pp 1100–1108

  • Ward JA, Grindrod P (2014) Aperiodic dynamics in a deterministic adaptive network model of attitude formation in social groups. Phys D Nonlinear Phenom 282:27–33

  • Watts DJ, Strogatz SH (1998) Collective dynamics of small-worldnetworks. Nature 393(6684):440–442

    Article  Google Scholar 

  • Weidlich W (2002) Sociodynamics-a systematic approach to mathematical modelling in the social sciences. Nonlinear Phenom Complex Syst Minsk 5(4):479–487

    MathSciNet  Google Scholar 

  • Weidlich W, Haag G (1983) Concepts and models of a quantitative sociology: the dynamics of interacting populations. Springer, Berlin 00007

    Book  MATH  Google Scholar 

  • Zeng X, Wei L (2013) Social ties and user content generation: evidence from flickr. Inf Syst Res 24(1):71–87

    Article  Google Scholar 

  • Zhou C, Kurths J (2006) Dynamical weights and enhanced synchronization in adaptive complex networks. Phys Rev Lett 96(16):164102

    Article  Google Scholar 

  • Zschaler G (2012) Adaptive-network models of collective dynamics. Euro Phys J Spec Top 211(1):1–101

    Article  Google Scholar 

Download references

Acknowledgments

The Authors wish to thank the Editors and the anonymous Reviewers for their detailed comments and suggestions which significantly contributed to the improvement of the manuscript.

Author information

Authors and Affiliations

  1. Department of Management Science and Technology, Athens University of Economics and Business, 47a Evelpidon Str., Athens, 11362, Greece

    Ilias N. Lymperopoulos & George D. Ioannou

Authors
  1. Ilias N. Lymperopoulos
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. George D. Ioannou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ilias N. Lymperopoulos.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lymperopoulos, I.N., Ioannou, G.D. Understanding and modeling the complex dynamics of the online social networks: a scalable conceptual approach. Evolving Systems 7, 207–232 (2016). https://doi.org/10.1007/s12530-016-9145-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12530-016-9145-9

Keywords

Search

Navigation

pFad - Phonifier reborn

Pfad - The Proxy pFad of © 2024 Garber Painting. All rights reserved.

Note: This service is not intended for secure transactions such as banking, social media, email, or purchasing. Use at your own risk. We assume no liability whatsoever for broken pages.


Alternative Proxies:

Alternative Proxy

pFad Proxy

pFad v3 Proxy

pFad v4 Proxy