Robust Modeling of Human Contact Networks Across Different Scales and Proximity-Sensing Techniques

  • Michele StarniniEmail author
  • Bruno Lepri
  • Andrea Baronchelli
  • Alain Barrat
  • Ciro Cattuto
  • Romualdo Pastor-Satorras
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10539)


The problem of mapping human close-range proximity networks has been tackled using a variety of technical approaches. Wearable electronic devices, in particular, have proven to be particularly successful in a variety of settings relevant for research in social science, complex networks and infectious diseases dynamics. Each device and technology used for proximity sensing (e.g., RFIDs, Bluetooth, low-power radio or infrared communication, etc.) comes with specific biases on the close-range relations it records. Hence it is important to assess which statistical features of the empirical proximity networks are robust across different measurement techniques, and which modeling frameworks generalize well across empirical data. Here we compare time-resolved proximity networks recorded in different experimental settings and show that some important statistical features are robust across all settings considered. The observed universality calls for a simplified modeling approach. We show that one such simple model is indeed able to reproduce the main statistical distributions characterizing the empirical temporal networks.


Social computing Computational social science Social network analysis Mobile sensing Mathematical modeling Wearable sensors 



M.S. acknowledges financial support from the James S. McDonnell Foundation. R.P.-S. acknowledges financial support from the Spanish MINECO, under projects FIS2013-47282-C2- 2 and FIS2016-76830-C2-1-P, and additional financial support from ICREA Academia, funded by the Generalitat de Catalunya. C.C. acknowledges support from the Lagrange Laboratory of the ISI Foundation funded by the CRT Foundation.


  1. 1.
    Aharony, N., Pan, W., Ip, C., Khayal, I., Pentland, A.: Social fMRI: investigating and shaping social mechanisms in the real world. Pervasive Mobile Comput. 7(6), 643–659 (2011)CrossRefGoogle Scholar
  2. 2.
    Alameda-Pineda, X., Staiano, J., Subramanian, R., Batrinca, L., Ricci, E., Lepri, B., Lanz, O., Sebe, N.: SALSA: a novel dataset for multimodal group behavior analysis. IEEE Trans. Pattern Anal. Mach. Intell. 38(8), 1707–1720 (2016)CrossRefGoogle Scholar
  3. 3.
    Alshamsi, A., Pianesi, F., Lepri, B., Pentland, A., Rahwan, I.: Beyond contagion: reality mining reveals complex patterns of social influence. Plos One 10(8), e0135740 (2015)CrossRefGoogle Scholar
  4. 4.
    Arrow, H., McGrath, J., Berdahl, J.: Small Groups as Complex Systems: Formation, Coordination, Development, and Adaptation. Sage-Publications, Thousand Oaks (2000)Google Scholar
  5. 5.
    Bales, R.: Interaction Process Analysis: A Method for the Study of Small Groups. Addison-Wesley, Boston (1950)Google Scholar
  6. 6.
    Barabasi, A.L.: The origin of bursts and heavy tails in human dynamics. Nature 435, 207 (2005)CrossRefGoogle Scholar
  7. 7.
    Bion, W.: Experiences in Groups and Other Papers. Routledge, Abingdon (2013)Google Scholar
  8. 8.
    Blondel, V., Decuyper, A., Krings, G.: A survey of results on mobile phone datasets analysis. EPJ Data Sci. 4, 10 (2015)CrossRefGoogle Scholar
  9. 9.
    Van den Broeck, W., Quaggiotto, M., Isella, L., Barrat, A., Cattuto, C.: The making of sixty-nine days of close encounters at the science gallery. Leonardo 45(3), 285 (2012)CrossRefGoogle Scholar
  10. 10.
    Cattuto, C., Van den Broeck, W., Barrat, A., Colizza, V., Pinton, J.F., Vespignani, A.: Dynamics of person-to-person interactions from distributed RFID sensor networks. Plos One 5(7), e11596 (2010)CrossRefGoogle Scholar
  11. 11.
    Cristani, M., Bazzani, L., Paggetti, G., Fossati, A., Tosato, D., Del Bue, A., Menegaz, G., Murino, V.: Social interaction discovery by statistical analysis of F-formations. In: Proceedings of the British Machine Vision Conference (2011)Google Scholar
  12. 12.
    Do, T., Gatica-Perez, D.: Human interaction discovery in smartphone proximity networks. Pers. Ubiquit. Comput. 3, 413–431 (2013)CrossRefGoogle Scholar
  13. 13.
    Do, T., Kalimeri, K., Lepri, B., Pianesi, F., Gatica-Perez, D.: Inferring social activities with mobile sensor networks. In: Proceedings of the International Conference on Multimodal Interaction, pp. 405–3412 (2013)Google Scholar
  14. 14.
    Doherty-Sneddon, G., Anderson, A., O’Malley, C., Langton, S., Garrod, S., Bruce, V.: Face-to-face and video-mediated communication: a comparison of dialogue structure and task performance. J. Exp. Psychol.: Appl. 3(2), 105–125 (1997)Google Scholar
  15. 15.
    Dong, W., Lepri, B., Pentland, A.: Modeling the co-evolution of behaviors and social relationships using mobile phone data. In: Proceedings of the Mobile and Ubiquitous Multimedia, pp. 134–143 (2011)Google Scholar
  16. 16.
    Eagle, N., Pentland, A.: Reality mining: sensing complex social systems. Pers. Ubiquit. Comput. 10(4), 255–268 (2006)CrossRefGoogle Scholar
  17. 17.
    Eagle, N., Pentland, A.S., Lazer, D.: Inferring friendship network structure by using mobile phone data. Proc. Natl. Acad. Sci. 106(36), 15274–15278 (2009)CrossRefGoogle Scholar
  18. 18.
    Eames, K., Bansal, S., Frost, S., Riley, S.: Six challenges in measuring contact networks for use in modelling. Epidemics 10, 72–77 (2015)CrossRefGoogle Scholar
  19. 19.
    Forys, A., Min, K., Schmid, T., Pettey, W., Toth, D., Leecaster, M.: WRENMining: large-scale data collection for human contact network research. In: Proceedings of the 1st International Workshop on Sensing and Big Data Mining, pp. 1–6 (2013)Google Scholar
  20. 20.
    Fournet, J., Barrat, A.: Contact patterns among high school students. Plos One 9(9), e107878 (2014)CrossRefGoogle Scholar
  21. 21.
    Gemmetto, V., Barrat, A., Cattuto, C.: Mitigation of infectious disease at school: targeted class closure vs school closure. BMC Infect. Dis. 14, 695 (2014)CrossRefGoogle Scholar
  22. 22.
    Gonzaléz, M., Hidalgo, C., Barabasi, A.L.: Understanding individual human mobility patterns. Nature 7196(453), 779–782 (2010)Google Scholar
  23. 23.
    Gnois, M., Vestergaard, C.L., Fournet, J., Panisson, A., Bonmarin, I., Barrat, A.: Data on face-to-face contacts in an office building suggest a low-cost vaccination strategy based on community linkers. Netw. Sci. 3(03), 326–347 (2015)CrossRefGoogle Scholar
  24. 24.
    Holme, P., Saramäki, J.: Temporal networks. Phys. Rep. 519, 97–125 (2012)CrossRefGoogle Scholar
  25. 25.
    Holme, P.: Modern temporal network theory: a colloquium. Eur. Phys. J. B 88(9), 234 (2015)CrossRefGoogle Scholar
  26. 26.
    Isella, L., Romano, M., Barrat, A., Cattuto, C., Colizza, V., Van den Broeck, W., Gesualdo, F., Pandolfi, E., Ravá, L., Rizzo, C., Tozzi, A.: Close encounters in a pediatric ward: measuring face-to-face proximity and mixing patterns with wearable sensors. Plos One 6(2), e17144 (2011)CrossRefGoogle Scholar
  27. 27.
    Isella, L., Stehlé, J., Barrat, A., Cattuto, C., Pinton, J.F., Van den Broeck, W.: What’s in a crowd? Analysis of face-to-face behavioral networks. J. Theor. Biol. 271(1), 166–180 (2011)MathSciNetCrossRefGoogle Scholar
  28. 28.
    Kendon, A., Harris, R., Key, R.: In: Hinds, P., Kiesler, S. (eds.) Organization of Behavior in Face-to-Face Interaction. De Gruyter Mouton, Berlin (1975)Google Scholar
  29. 29.
    Kraut, R., Fish, R., Root, R., Chalfonte, B.: Informal communication in organizations: form, function, and technology. In: Groupware and Computer-Supported Cooperative Work, pp. 287–314 (1993)Google Scholar
  30. 30.
    Lepri, B., Staiano, J., Rigato, G., Kalimeri, K., Finnerty, A., Pianesi, F., Sebe, N., Pentland, A.: The SocioMetric badges corpus: a multilevel behavioral dataset for social behavior in complex organizations. In: IEEE Proceedings of SocialCom/PASSAT, pp. 623–628. IEEE (2012)Google Scholar
  31. 31.
    Liljeros, F., Edling, C., Amaral, L., Stanley, H., Aberg, Y.: The web of human sexual contacts. Nature 6840, 907–908 (2001)CrossRefGoogle Scholar
  32. 32.
    Madan, A., Cebrian, M., Lazer, D., Pentland, A.: Social sensing for epidemiological behavior change. In: Proceedings of the ACM International Conference on Ubiquitous Computing, pp. 291–300 (2010)Google Scholar
  33. 33.
    Madan, A., Cebrian, M., Moturu, S., Farrahi, K., Pentland, A.: Sensing the “health state” of a community. IEEE Pervasive Comput. 11(4), 36–45 (2012)CrossRefGoogle Scholar
  34. 34.
    Nardi, B., Whittaker, S.: The place of face to face communication in distributed work. In: Hinds, P., Kiesler, S. (eds.) Distributed Work, pp. 351–360. MIT Press, Cambridge (2002)Google Scholar
  35. 35.
    Nohria, N., Eccles, R.: Face-to-face: making network organizations work. In: Technology, Organizations and Innovation: Critical Perspectives on Business and Management, pp. 1659–1681 (2000)Google Scholar
  36. 36.
    Olguín, O.D., Waber, B., Kim, T., Mohan, A., Ara, K., Pentland, A.: Sensible organizations technology and methodology for automatically measuring organizational behavior. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 39, 43–55 (2009)CrossRefGoogle Scholar
  37. 37.
    Onnela, J.P., Waber, B.N., Pentland, A., Schnorf, S., Lazer, D.: Using sociometers to quantify social interaction patterns. Sci. Rep. 4 (2014). Article no. 5604Google Scholar
  38. 38.
    Ribeiro, B., Perra, N., Baronchelli, A.: Quantifying the effect of temporal resolution on time-varying networks. Sci. Rep. 3 (2013). Article no. 3006Google Scholar
  39. 39.
    Salathé, M., Kazandjieva, M., Lee, J.W., Levis, P., Feldman, M.W., Jones, J.H.: A high-resolution human contact network for infectious disease transmission. Proc. Natl. Acad. Sci. 107(51), 22020–22025 (2010)CrossRefGoogle Scholar
  40. 40.
    Smieszek, T., Salathé, M.: A low-cost method to assess the epidemiological importance of individuals in controlling infectious disease outbreaks. BMC Med. 11(1), 35 (2013). CrossRefGoogle Scholar
  41. 41.
    Starnini, M., Baronchelli, A., Pastor-Satorras, R.: Modeling human dynamics of face-to-face interaction networks. Phys. Rev. Lett. 110, 168701–168706 (2013)CrossRefGoogle Scholar
  42. 42.
    Starnini, M., Baronchelli, A., Barrat, A., Pastor-Satorras, R.: Random walks on temporal networks. Phys. Rev. E 85, 056115 (2012)CrossRefGoogle Scholar
  43. 43.
    Starnini, M., Baronchelli, A., Pastor-Satorras, R.: Model reproduces individual, group and collective dynamics of human contact networks. Soc. Netw. 47, 130–137 (2016)CrossRefGoogle Scholar
  44. 44.
    Starnini, M., Machens, A., Cattuto, C., Barrat, A., Pastor-Satorras, R.: Immunization strategies for epidemic processes in time-varying contact networks. J. Theor. Biol. 337, 89–100 (2013)MathSciNetCrossRefGoogle Scholar
  45. 45.
    Stehlé, J., Voirin, N., Barrat, A., Cattuto, C., Colizza, V., Isella, L., Régis, C., Pinton, J.F., Khanafer, N., Van den Broeck, W., Vanhems, P.: Simulation of an SEIR infectious disease model on the dynamic contact network of conference attendees. BMC Med. 9, 87 (2011)CrossRefGoogle Scholar
  46. 46.
    Stehlé, J., Voirin, N., Barrat, A., Cattuto, C., Isella, L., Pinton, J.F., Quaggiotto, M., Van den Broeck, W., Régis, C., Lina, B., Vanhems, P.: High-resolution measurements of face-to-face contact patterns in a primary school. Plos One 6(8), e23176 (2011)CrossRefGoogle Scholar
  47. 47.
    Stopczynski, A., Sekara, V., Sapiezynski, P., Cuttone, A., Larsen, J.E., Lehmann, S.: Measuring large-scale social networks with high resolution. PLOS One 9(4), e95978 (2014)CrossRefGoogle Scholar
  48. 48.
    Storper, M., Venables, A., Pastor-Satorras, R.: Buzz: face-to-face contact and the urban economy. J. Econ. Geogr. 4, 351–360 (2004)CrossRefGoogle Scholar
  49. 49.
    Toth, D.J.A., Leecaster, M., Pettey, W.B.P., Gundlapalli, A.V., Gao, H., Rainey, J.J., Uzicanin, A., Samore, M.H.: The role of heterogeneity in contact timing and duration in network models of influenza spread in schools. J. R. Soc. Interface 12(108), 20150279 (2015)CrossRefGoogle Scholar
  50. 50.
    Whittaker, S., Frohlich, D., Daly-Jones, O.: Informal workplace communication: what is it like and how might we support it? In: Proceedings of the ACM Conference on Human Factors in Computing Systems CHI 1994, pp. 131–137. ACM Press, New York (1994)Google Scholar
  51. 51.
    Wright, M., Li, Y.: The associations between young adults’ face-to-face prosocial behaviorsand their online prosocial behaviors. Comput. Hum. Behav. 27, 1959–1961 (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Michele Starnini
    • 1
    • 2
    Email author
  • Bruno Lepri
    • 3
  • Andrea Baronchelli
    • 4
  • Alain Barrat
    • 5
    • 6
  • Ciro Cattuto
    • 5
  • Romualdo Pastor-Satorras
    • 7
  1. 1.Departament de Física FonamentalUniversitat de BarcelonaBarcelonaSpain
  2. 2.Universitat de Barcelona Institute of Complex Systems (UBICS)Universitat de BarcelonaBarcelonaSpain
  3. 3.Fondazione Bruno KesslerTrentoItaly
  4. 4.Department of MathematicsCity, University of LondonLondonUK
  5. 5.ISI FoundationTorinoItaly
  6. 6.Aix Marseille UnivUniversité de Toulon, CNRS, CPTMarseilleFrance
  7. 7.Departament de FísicaUniversitat Politàcnica de CatalunyaBarcelonaSpain

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