2018
Journal article
Open Access
NDlib: a python library to model and analyze diffusion processes over complex networks
Rossetti G., Milli L., Rinzivillo S., Sirbu A., Giannotti F., Pedreschi D.
Epidemics Modeling and Simulation 60J60 05C85 Social and Information Networks (cs.SI) Complex Networks Information Systems Computer Science - Social and Information Networks Computational Theory and Mathematics G.2.2 FOS: Computer and information sciences 90C35 Computer Science Applications Social network analysis software Diffusion Applied Mathematics F.2.1 Opinion dynamics
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.
Source: International Journal of Data Science and Analytics (Online) 5 (2018): 61–79. doi:10.1007/s41060-017-0086-6
Publisher: Springer
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4. Casteigts, A., Flocchini, P., Quattrociocchi, W., Santoro, N.: Time-varying graphs and dynamic networks. International Journal of Parallel, Emergent and Distributed Systems 27(5), 387{408 (2012)
5. Castellano, C., Munoz, M.A., Pastor-Satorras, R.: The non-linear q-voter model. Physical Review E 80, 041,129 (2009)
6. Chao, D.L., Halloran, M.E., Obenchain, V.J., Longini Jr, I.M.: Flute, a publicly available stochastic in uenza epidemic simulation model. PLoS computational biology 6(1), e1000,656 (2010)
7. Cli ord, P., Sudbury, A.: A model for spatial con ict. Biometrika 60(3), 581{588 (1973). DOI 10.1093/biomet/ 60.3.581
8. Coelho, F.C., Cruz, O.G., Codeco, C.T.: Epigrass: a tool to study disease spread in complex networks. Source code for biology and medicine 3(1), 3 (2008)
9. De uant, G., Neau, D., Amblard, F., Weisbuch, G.: Mixing beliefs among interacting agents. Advances in Complex Systems 3(4), 87{98 (2000)
10. Friedman, R., Friedman, M.: The Tyranny of the Status Quo. Harcourt Brace Company, Orlando, FL, USA (1984)
11. Galam, S.: Minority opinion spreading in random geometry. Eur. Phys. J. B 25(4), 403{406 (2002)
12. Granovetter, M.: Threshold models of collective behavior. The American Journal of Sociology 83(6), 1420{1443 (1978)
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)
14. Havlin, S.: Phone infections. Science (2009)
15. Holley, R., Liggett, T.: Ergodic theorems for weakly interacting in nite systems and the voter model. Ann. Probab. 3(4), 643{663 (1975)
16. Holme, P., Saramaki, J.: Temporal networks. Physics reports 519(3), 97{125 (2012)
17. Jenness, S., Goodreau, S.M., Morris, M.: Epimodel: Mathematical modeling of infectious disease. r package version 1.3.0. (2017). URL http://www.epimodel.org
18. Kempe, D., Kleinberg, J., Tardos, E.: Maximizing the spread of in uence 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)
19. Kermack, W.O., McKendrick, A.: A Contribution to the Mathematical Theory of Epidemics. Proceedings of the Royal Society of London. Series A, Containing Papers of a Mathematical and Physical Character 115(772), 700{ 721 (1927)
20. Kiss, I.Z., Miller, J.C., Simon, P.: (Book) Mathematics of epidemics on networks: from exact to approximate models. Springer (Forthcoming)
21. Kovanen, L., Karsai, M., Kaski, K., Kertesz, J., Saramaki, J.: Temporal motifs in time-dependent networks. Journal of Statistical Mechanics: Theory and Experiment 2011(11), P11,005 (2011)
22. Krapivsky, P.L., Redner, S., Ben-Naim, E.: A kinetic view of statistical physics. Cambridge University Press (2010)
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)
24. Leskovec, J., Sosic, R.: Snap: A general-purpose network analysis and graph-mining library. ACM Transactions on Intelligent Systems and Technology (TIST) 8(1), 1 (2016)
25. Milli, L., Rossetti, G., Pedreschi, D., Giannotti, F.: Information di usion in complex networks: The active/passive conundrum. In: Complex Networks (2017)
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)
27. Newton, C.M.: Graphics: from alpha to omega in data analysis. In: P.C. Wang (ed.) Graphical Representation of Multivariate Data, pp. 59 { 92. Academic Press (1978). DOI 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)
29. Rossetti, G.: Rdyn: Graph benchmark handling community dynamics. Journal of Complex Networks (2017)
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-/intercommunity interaction prediction in dynamic social networks. Social Network Analysis and Mining 6(1), 86 (2016)
32. Rossetti, G., Pappalardo, L., Pedreschi, D., Giannotti, F.: Tiles: an online algorithm for community discovery in dynamic social networks. Machine Learning pp. 1{29 (2016)
33. Ruan, Z., In~iguez, G., Karsai, M., Kertesz, J.: Kinetics of social contagion. Phys. Rev. Lett. 115, 218,702 (2015). DOI 10.1103/PhysRevLett.115.218702
34. Sahneh, F.D., Vajdi, A., Shakeri, H., Fan, F., Scoglio, C.: Gemfsim: a stochastic simulator for the generalized epidemic modeling framework. Journal of Computational Science 22, 36{44 (2017)
35. S^rbu, A., Loreto, V., Servedio, V.D., Tria, F.: Opinion dynamics with disagreement and modulated information. Journal of Statistical Physics pp. 1{20 (2013)
36. S^rbu, A., Loreto, V., Servedio, V.D., Tria, F.: Opinion dynamics: Models, extensions and external e ects. In: Participatory Sensing, Opinions and Collective Awareness, pp. 363{401. Springer International Publishing (2017)
37. Sznajd-Weron, K., Sznajd, J.: Opinion evolution in closed community. International Journal of Modern Physics C 11, 1157{1165 (2001)
38. Szor, P.: Fighting computer virus attacks. USENIX (2004)
39. Tabourier, L., Libert, A.S., Lambiotte, R.: Predicting links in ego-networks using temporal information. EPJ Data Science 5(1), 1 (2016)
40. Viard, T., Latapy, M., Magnien, C.: Computing maximal cliques in link streams. Theoretical Computer Science 609, 245{252 (2016)
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., Gonzalez, M.C., Menezes, R., Barabasi, A.L.: Understanding the spread of malicious mobile-phone programs and their damage potential. International Journal of Information Security (2013)
43. Watts, D.J.: A simple model of global cascades on random networks. Proceedings of the National Academy of Sciences 99(9), 5766{5771 (2002)
44. Wilensky, U.: Netlogo (1999)
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)
2. Van den Broeck, W., Gioannini, C., Goncalves, 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 infectious diseases 11(1), 37 (2011)
3. Burt, R.S.: Social Contagion and Innovation: Cohesion Versus Structural Equivalence. American Journal of Sociology (1987)
4. Casteigts, A., Flocchini, P., Quattrociocchi, W., Santoro, N.: Time-varying graphs and dynamic networks. International Journal of Parallel, Emergent and Distributed Systems 27(5), 387{408 (2012)
5. Castellano, C., Munoz, M.A., Pastor-Satorras, R.: The non-linear q-voter model. Physical Review E 80, 041,129 (2009)
6. Chao, D.L., Halloran, M.E., Obenchain, V.J., Longini Jr, I.M.: Flute, a publicly available stochastic in uenza epidemic simulation model. PLoS computational biology 6(1), e1000,656 (2010)
7. Cli ord, P., Sudbury, A.: A model for spatial con ict. Biometrika 60(3), 581{588 (1973). DOI 10.1093/biomet/ 60.3.581
8. Coelho, F.C., Cruz, O.G., Codeco, C.T.: Epigrass: a tool to study disease spread in complex networks. Source code for biology and medicine 3(1), 3 (2008)
9. De uant, G., Neau, D., Amblard, F., Weisbuch, G.: Mixing beliefs among interacting agents. Advances in Complex Systems 3(4), 87{98 (2000)
10. Friedman, R., Friedman, M.: The Tyranny of the Status Quo. Harcourt Brace Company, Orlando, FL, USA (1984)
11. Galam, S.: Minority opinion spreading in random geometry. Eur. Phys. J. B 25(4), 403{406 (2002)
12. Granovetter, M.: Threshold models of collective behavior. The American Journal of Sociology 83(6), 1420{1443 (1978)
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)
14. Havlin, S.: Phone infections. Science (2009)
15. Holley, R., Liggett, T.: Ergodic theorems for weakly interacting in nite systems and the voter model. Ann. Probab. 3(4), 643{663 (1975)
16. Holme, P., Saramaki, J.: Temporal networks. Physics reports 519(3), 97{125 (2012)
17. Jenness, S., Goodreau, S.M., Morris, M.: Epimodel: Mathematical modeling of infectious disease. r package version 1.3.0. (2017). URL http://www.epimodel.org
18. Kempe, D., Kleinberg, J., Tardos, E.: Maximizing the spread of in uence 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)
19. Kermack, W.O., McKendrick, A.: A Contribution to the Mathematical Theory of Epidemics. Proceedings of the Royal Society of London. Series A, Containing Papers of a Mathematical and Physical Character 115(772), 700{ 721 (1927)
20. Kiss, I.Z., Miller, J.C., Simon, P.: (Book) Mathematics of epidemics on networks: from exact to approximate models. Springer (Forthcoming)
21. Kovanen, L., Karsai, M., Kaski, K., Kertesz, J., Saramaki, J.: Temporal motifs in time-dependent networks. Journal of Statistical Mechanics: Theory and Experiment 2011(11), P11,005 (2011)
22. Krapivsky, P.L., Redner, S., Ben-Naim, E.: A kinetic view of statistical physics. Cambridge University Press (2010)
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)
24. Leskovec, J., Sosic, R.: Snap: A general-purpose network analysis and graph-mining library. ACM Transactions on Intelligent Systems and Technology (TIST) 8(1), 1 (2016)
25. Milli, L., Rossetti, G., Pedreschi, D., Giannotti, F.: Information di usion in complex networks: The active/passive conundrum. In: Complex Networks (2017)
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)
27. Newton, C.M.: Graphics: from alpha to omega in data analysis. In: P.C. Wang (ed.) Graphical Representation of Multivariate Data, pp. 59 { 92. Academic Press (1978). DOI 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)
29. Rossetti, G.: Rdyn: Graph benchmark handling community dynamics. Journal of Complex Networks (2017)
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-/intercommunity interaction prediction in dynamic social networks. Social Network Analysis and Mining 6(1), 86 (2016)
32. Rossetti, G., Pappalardo, L., Pedreschi, D., Giannotti, F.: Tiles: an online algorithm for community discovery in dynamic social networks. Machine Learning pp. 1{29 (2016)
33. Ruan, Z., In~iguez, G., Karsai, M., Kertesz, J.: Kinetics of social contagion. Phys. Rev. Lett. 115, 218,702 (2015). DOI 10.1103/PhysRevLett.115.218702
34. Sahneh, F.D., Vajdi, A., Shakeri, H., Fan, F., Scoglio, C.: Gemfsim: a stochastic simulator for the generalized epidemic modeling framework. Journal of Computational Science 22, 36{44 (2017)
35. S^rbu, A., Loreto, V., Servedio, V.D., Tria, F.: Opinion dynamics with disagreement and modulated information. Journal of Statistical Physics pp. 1{20 (2013)
36. S^rbu, A., Loreto, V., Servedio, V.D., Tria, F.: Opinion dynamics: Models, extensions and external e ects. In: Participatory Sensing, Opinions and Collective Awareness, pp. 363{401. Springer International Publishing (2017)
37. Sznajd-Weron, K., Sznajd, J.: Opinion evolution in closed community. International Journal of Modern Physics C 11, 1157{1165 (2001)
38. Szor, P.: Fighting computer virus attacks. USENIX (2004)
39. Tabourier, L., Libert, A.S., Lambiotte, R.: Predicting links in ego-networks using temporal information. EPJ Data Science 5(1), 1 (2016)
40. Viard, T., Latapy, M., Magnien, C.: Computing maximal cliques in link streams. Theoretical Computer Science 609, 245{252 (2016)
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., Gonzalez, M.C., Menezes, R., Barabasi, A.L.: Understanding the spread of malicious mobile-phone programs and their damage potential. International Journal of Information Security (2013)
43. Watts, D.J.: A simple model of global cascades on random networks. Proceedings of the National Academy of Sciences 99(9), 5766{5771 (2002)
44. Wilensky, U.: Netlogo (1999)
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)
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BibTeX entry
@article{oai:it.cnr:prodotti:384729,
title = {NDlib: a python library to model and analyze diffusion processes over complex networks},
author = {Rossetti G. and Milli L. and Rinzivillo S. and Sirbu A. and Giannotti F. and Pedreschi D.},
publisher = {Springer},
doi = {10.1007/s41060-017-0086-6 and 10.48550/arxiv.1801.05854},
journal = {International Journal of Data Science and Analytics (Online)},
volume = {5},
pages = {61–79},
year = {2018}
}
0000-0003-3099-3835
