# NDlib: a python library to model and analyze diffusion processes over complex networks

- 1.3k Downloads
- 1 Citations

## Abstract

Nowadays the analysis of dynamics of and on networks represents a hot topic in the social network analysis playground. To support students, teachers, developers and researchers, in this work we introduce a novel framework, namely NDlib, an environment designed to describe diffusion simulations. NDlib is designed to be a multi-level ecosystem that can be fruitfully used by different user segments. For this reason, upon NDlib, we designed a simulation server that allows remote execution of experiments as well as an online visualization tool that abstracts its programmatic interface and makes available the simulation platform to non-technicians.

## Keywords

Social network analysis software Epidemics Opinion dynamics## Notes

### Acknowledgements

This work is funded by the European Community’s H2020 Program under the funding scheme “FETPROACT-1-2014: Global Systems Science (GSS),” grant agreement # 641191 CIMPLEX “Bringing CItizens, Models and Data together in Participatory, Interactive SociaL EXploratories” (CIMPLEX:https://www.cimplex-project.eu). This work is supported by the European Community’s H2020 Program under the scheme “INFRAIA-1-2014-2015: Research Infrastructures", grant agreement #654024 *“SoBigData: Social Mining & Big Data Ecosystem"* (SoBigData: http://www.sobigdata.eu).

### Compliance with ethical standards

### Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest

## References

- 1.Ahrenberg, L., Kok, S., Vasarhelyi, K., Rutherford, A.: Nepidemix (2016)Google Scholar
- 2.Van den Broeck, W., Gioannini, C., Gonçalves, B., Quaggiotto, M., Colizza, V., Vespignani, A.: The gleamviz computational tool, a publicly available software to explore realistic epidemic spreading scenarios at the global scale. BMC Infect Dis
**11**(1), 37 (2011)CrossRefGoogle Scholar - 3.Burt, R.S.: Social contagion and innovation: cohesion versus structural equivalence. Am. J. Sociol.
**92**, 1287 (1987)CrossRefGoogle Scholar - 4.Casteigts, A., Flocchini, P., Quattrociocchi, W., Santoro, N.: Time-varying graphs and dynamic networks. Int. J. Parallel Emerg. Distrib. Syst.
**27**(5), 387–408 (2012)CrossRefGoogle Scholar - 5.Castellano, C., Munoz, M.A., Pastor-Satorras, R.: The non-linear q-voter model. Phys. Rev. E
**80**, 041–129 (2009)CrossRefGoogle Scholar - 6.Chao, D.L., Halloran, M.E., Obenchain, V.J., Longini Jr., I.M.: Flute, a publicly available stochastic influenza epidemic simulation model. PLoS Comput. Biol.
**6**(1), e1000–656 (2010)MathSciNetCrossRefGoogle Scholar - 7.Clifford, P., Sudbury, A.: A model for spatial conflict. Biometrika
**60**(3), 581–588 (1973). https://doi.org/10.1093/biomet/60.3.581 MathSciNetCrossRefzbMATHGoogle Scholar - 8.Coelho, F.C., Cruz, O.G., Codeço, C.T.: Epigrass: a tool to study disease spread in complex networks. Sour. Code Biol. Med.
**3**(1), 3 (2008)CrossRefGoogle Scholar - 9.Deffuant, G., Neau, D., Amblard, F., Weisbuch, G.: Mixing beliefs among interacting agents. Adv. Complex Syst.
**3**(4), 87–98 (2000)CrossRefGoogle Scholar - 10.Friedman, R., Friedman, M.: The Tyranny of the Status Quo. Harcourt Brace Company, Orlando (1984)Google Scholar
- 11.Galam, S.: Minority opinion spreading in random geometry. Eur. Phys. J. B
**25**(4), 403–406 (2002)Google Scholar - 12.Granovetter, M.: Threshold models of collective behavior. Am. J. Sociol.
**83**(6), 1420–1443 (1978)CrossRefGoogle Scholar - 13.Grefenstette, J.J., Brown, S.T., Rosenfeld, R., DePasse, J., Stone, N.T., Cooley, P.C., Wheaton, W.D., Fyshe, A., Galloway, D.D., Sriram, A., et al.: Fred (a framework for reconstructing epidemic dynamics): an open-source software system for modeling infectious diseases and control strategies using census-based populations. BMC Public Health
**13**(1), 940 (2013)CrossRefGoogle Scholar - 14.Havlin, S.: Phone infections. Science
**324**, 1023 (2009)CrossRefGoogle Scholar - 15.Holley, R., Liggett, T.: Ergodic theorems for weakly interacting infinite systems and the voter model. Ann. Probab.
**3**(4), 643–663 (1975)MathSciNetCrossRefzbMATHGoogle Scholar - 16.Holme, P., Saramäki, J.: Temporal networks. Phys. Rep.
**519**(3), 97–125 (2012)CrossRefGoogle Scholar - 17.Jenness, S., Goodreau, S.M., Morris, M.: Epimodel: Mathematical modeling of infectious disease. r package version 1.3.0. (2017). http://www.epimodel.org
- 18.Kempe, D., Kleinberg, J., Tardos, E.: Maximizing the spread of influence through a social network. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’03, pp. 137–146 (2003)Google Scholar
- 19.Kermack, W.O., McKendrick, A.: A contribution to the mathematical theory of epidemics. Proceed. R. Soc. Lond. Ser. A Contain. Papers Math. Phys. Character
**115**(772), 700–721 (1927)CrossRefzbMATHGoogle Scholar - 20.Kiss, I.Z., Miller, J.C., Simon, P.: Mathematics of Epidemics on Networks: From Exact to Approximate Models. Springer (Forthcoming)Google Scholar
- 21.Kovanen, L., Karsai, M., Kaski, K., Kertész, J., Saramäki, J.: Temporal motifs in time-dependent networks. J. Statist. Mech. Theory Exp.
**2011**(11), p11005 (2011)CrossRefGoogle Scholar - 22.Krapivsky, P.L., Redner, S., Ben-Naim, E.: A Kinetic View of Statistical Physics. Cambridge University Press, Cambridge (2010)CrossRefzbMATHGoogle Scholar
- 23.Lee, S., Rocha, L.E., Liljeros, F., Holme, P.: Exploiting temporal network structures of human interaction to effectively immunize populations. PLoS ONE
**7**(5), e36–439 (2012)Google Scholar - 24.Leskovec, J., Sosič, R.: Snap: a general-purpose network analysis and graph-mining library. ACM Trans. Intell. Syst. Technol. (TIST)
**8**(1), 1 (2016)CrossRefGoogle Scholar - 25.Milli, L., Rossetti, G., Pedreschi, D., Giannotti, F.: Information diffusion in complex networks: The active/passive conundrum. In: Complex Networks (2017)Google Scholar
- 26.Milli, L., Rossetti, G., Pedreschi, D., Giannotti, F.: Diffusive phenomena in dynamic networks: a data-driven study. In: 9th Conference on Complex Networks, CompleNet (2018)Google Scholar
- 27.Newton, C.M.: Graphics: from alpha to omega in data analysis. In: Wang, P.C. (ed.) Graphical Representation of Multivariate Data, pp. 59–92. Academic Press (1978). https://doi.org/10.1016/B978-0-12-734750-9.50008-3 URL http://www.sciencedirect.com/science/article/pii/B9780127347509500083
- 28.Pennacchioli, D., Rossetti, G., Pappalardo, L., Pedreschi, D., Giannotti, F., Coscia, M.: The three dimensions of social prominence. In: International Conference on Social Informatics, pp. 319–332. Springer (2013)Google Scholar
- 29.Rossetti, G.: Rdyn: graph benchmark handling community dynamics. J. Complex Netw.
**5**, 893 (2017)CrossRefGoogle Scholar - 30.Rossetti, G., Cazabet, R.: Community discovery in dynamic networks: a survey. arXiv preprint arXiv:1707.03186 (2017)
- 31.Rossetti, G., Guidotti, R., Miliou, I., Pedreschi, D., Giannotti, F.: A supervised approach for intra-/inter-community interaction prediction in dynamic social networks. Soc. Netw. Anal. Min.
**6**(1), 86 (2016)CrossRefGoogle Scholar - 32.Rossetti, G., Pappalardo, L., Pedreschi, D., Giannotti, F.: Tiles: an online algorithm for community discovery in dynamic social networks. Mach. Learn. pp. 1–29 (2016)Google Scholar
- 33.Ruan, Z., Iñiguez, G., Karsai, M., Kertész, J.: Kinetics of social contagion. Phys. Rev. Lett
**115**, 218702 (2015). https://doi.org/10.1103/PhysRevLett.115.218702 CrossRefGoogle Scholar - 34.Sahneh, F.D., Vajdi, A., Shakeri, H., Fan, F., Scoglio, C.: Gemfsim: a stochastic simulator for the generalized epidemic modeling framework. J. Comput. Sci.
**22**, 36–44 (2017)CrossRefGoogle Scholar - 35.Sîrbu, A., Loreto, V., Servedio, V.D., Tria, F.: Opinion dynamics with disagreement and modulated information. J. Stat. Phys.
**151**, 1–20 (2013)MathSciNetCrossRefzbMATHGoogle Scholar - 36.Sîrbu, A., Loreto, V., Servedio, V.D., Tria, F.: Opinion dynamics: models, extensions and external effects. In: Participatory Sensing, Opinions and Collective Awareness, pp. 363–401. Springer International Publishing (2017)Google Scholar
- 37.Sznajd-Weron, K., Sznajd, J.: Opinion evolution in closed community. Int. J. Mod. Phys. C
**11**, 1157–1165 (2001)CrossRefzbMATHGoogle Scholar - 38.Szor, P.: Fighting Computer Virus Attacks. USENIX, Berkeley (2004)Google Scholar
- 39.Tabourier, L., Libert, A.S., Lambiotte, R.: Predicting links in ego-networks using temporal information. EPJ Data Sci.
**5**(1), 1 (2016)CrossRefGoogle Scholar - 40.Viard, T., Latapy, M., Magnien, C.: Computing maximal cliques in link streams. Theor. Comput. Sci.
**609**, 245–252 (2016)MathSciNetCrossRefzbMATHGoogle Scholar - 41.Vilone, D., Giardini, F., Paolucci, M., Conte, R.: Reducing individuals’ risk sensitiveness can promote positive and non-alarmist views about catastrophic events in an agent-based simulation. arXiv preprint arXiv:1609.04566 (2016)
- 42.Wang, P., González, M.C., Menezes, R., Barabási, A.L.: Understanding the spread of malicious mobile-phone programs and their damage potential. Int. J. Inf. Secur.
**12**, 383 (2013)CrossRefGoogle Scholar - 43.Watts, D.J.: A simple model of global cascades on random networks. Proc. Natl. Acad. Sci.
**99**(9), 5766–5771 (2002)MathSciNetCrossRefzbMATHGoogle Scholar - 44.Wilensky, U.: Netlogo (1999)Google Scholar
- 45.Word, D.P., Abbott, G.H., Cummings, D., Laird, C.D.: Estimating seasonal drivers in childhood infectious diseases with continuous time and discrete-time models. In: American Control Conference (ACC), 2010, pp. 5137–5142. IEEE (2010)Google Scholar