J. M. Read, W. J. Edmunds, S. Riley, J. Lessler, and D. A. Cummings, Close encounters of the infectious kind: methods to measure social mixing behaviour, Epidemiology and Infection, vol.5, issue.12, pp.2117-2130, 2012.
DOI : 10.3201/eid1211.060426

W. J. Edmunds, C. J. O-'callaghan, and D. J. Nokes, Who mixes with whom? A method to determine the contact patterns of adults that may lead to the spread of airborne infections, Proceedings of the Royal Society of London. Series B: Biological Sciences, pp.949-957, 1997.
DOI : 10.1098/rspb.1997.0131

J. Read, K. Eames, and W. Edmunds, Dynamic social networks and the implications for the spread of infectious disease, Journal of The Royal Society Interface, vol.28, issue.1, pp.1001-1008, 2008.
DOI : 10.1038/30918

J. Mossong, Social Contacts and Mixing Patterns Relevant to the Spread of Infectious Diseases, PLoS Medicine, vol.19, issue.3, p.74, 2008.
DOI : 10.1371/journal.pmed.0050074.sd001

R. Mikolajczyk, M. Akmatov, S. Rastin, and M. Kretzschmar, Social contacts of school children and the transmission of respiratory-spread pathogens, Epidemiology and Infection, vol.12, issue.06, pp.813-822, 2008.
DOI : 10.1017/S0950268805004528

L. Danon, T. House, J. Read, and M. Keeling, Social encounter networks: collective properties and disease transmission, Journal of The Royal Society Interface, vol.272, issue.1, pp.2826-2833, 2012.
DOI : 10.1016/j.jtbi.2010.12.009

L. Danon, J. M. Read, T. A. House, M. C. Vernon, and M. J. Keeling, Social encounter networks: characterizing Great Britain, Proceedings of the Royal Society B: Biological Sciences, vol.438, issue.7066, 2013.
DOI : 10.1038/nature04153

T. Smieszek, E. U. Burri, R. Scherzinger, and R. W. Scholz, Collecting close-contact social mixing data with contact diaries: reporting errors and biases, Epidemiology and Infection, vol.140, issue.04, pp.744-752, 2012.
DOI : 10.1371/journal.pone.0011596

T. Smieszek, How should social mixing be measured: comparing web-based survey and sensor-based methods, BMC Infectious Diseases, vol.8, issue.Suppl 5, p.136, 2014.
DOI : 10.1186/1471-2458-8-61

P. Hui, Pocket switched networks and human mobility in conference environments, Proceeding of the 2005 ACM SIGCOMM workshop on Delay-tolerant networking , WDTN '05, 2005.
DOI : 10.1145/1080139.1080142

O. Neill and E. , Instrumenting the city: Developing methods for observing and understanding the digital cityscape, Ubicomp, pp.315-332, 2006.

N. Eagle, A. S. Pentland, and D. Lazer, Inferring friendship network structure by using mobile phone data, Proceedings of the National Academy of Sciences, vol.106, issue.36, pp.15274-15278, 2009.
DOI : 10.1073/pnas.0900282106

L. Vu, K. Nahrstedt, S. Retika, and I. Gupta, Joint bluetooth/wifi scanning framework for characterizing and leveraging people movement in university campus, Proceedings of the 13th ACM international conference on Modeling, analysis, and simulation of wireless and mobile systems, MSWIM '10, pp.257-265, 2010.
DOI : 10.1145/1868521.1868563

M. Salathé, A high-resolution human contact network for infectious disease transmission, Proceedings of the National Academy of Sciences, vol.107, issue.51, pp.22020-22025, 2010.
DOI : 10.1073/pnas.1009094108

M. Hashemian, K. Stanley, N. Osgood, and . Flunet, Automated tracking of contacts during flu season, Proceedings of the 6th International workshop on Wireless Network Measurements, pp.557-562, 2010.
URL : https://hal.archives-ouvertes.fr/inria-00498447

C. Cattuto, Dynamics of Person-to-Person Interactions from Distributed RFID Sensor Networks, PLoS ONE, vol.41, issue.7, p.11596, 2010.
DOI : 10.1371/journal.pone.0011596.s007
URL : https://hal.archives-ouvertes.fr/hal-00503275

T. Hornbeck, Using Sensor Networks to Study the Effect of Peripatetic Healthcare Workers on the Spread of Hospital-Associated Infections, Journal of Infectious Diseases, vol.206, issue.10, 2012.
DOI : 10.1093/infdis/jis542

A. Stopczynski, Measuring Large-Scale Social Networks with High Resolution, PLoS ONE, vol.28, issue.4, p.95978, 2014.
DOI : 10.1371/journal.pone.0095978.g015

L. Isella, What's in a crowd? Analysis of face-to-face behavioral networks, Journal of Theoretical Biology, vol.271, issue.1, pp.166-180, 2011.
DOI : 10.1016/j.jtbi.2010.11.033

J. Stehlé, Simulation of an SEIR infectious disease model on the dynamic contact network of conference attendees, BMC Medicine, vol.6, issue.Suppl 5, 2011.
DOI : 10.1371/journal.pone.0017144

J. Stehlé, High-Resolution Measurements of Face-to-Face Contact Patterns in a Primary School, PLoS ONE, vol.3, issue.2, p.23176, 2011.
DOI : 10.1371/journal.pone.0023176.s005

J. Fournet and A. Barrat, Contact Patterns among High School Students, PLoS ONE, vol.59, issue.9, p.107878, 2014.
DOI : 10.1371/journal.pone.0107878.s001
URL : https://hal.archives-ouvertes.fr/hal-01065922

P. Holme and J. Saramki, Temporal networks, Physics Reports, vol.519, issue.3, pp.97-125, 2012.
DOI : 10.1016/j.physrep.2012.03.001

S. Lee, L. E. Rocha, F. Liljeros, and P. Holme, Exploiting Temporal Network Structures of Human Interaction to Effectively Immunize Populations, PLoS ONE, vol.6, issue.5, p.36439, 2012.
DOI : 10.1371/journal.pone.0036439.s005

A. Machens, An infectious disease model on empirical networks of human contact: bridging the gap between dynamic network data and contact matrices, BMC Infectious Diseases, vol.5, issue.1, p.185, 2013.
DOI : 10.1371/journal.pcbi.1001109
URL : https://hal.archives-ouvertes.fr/hal-00817269

M. Starnini, A. Machens, C. Cattuto, A. Barrat, and R. Pastor-satorras, Immunization strategies for epidemic processes in time-varying contact networks, Journal of Theoretical Biology, vol.337, pp.89-100, 2013.
DOI : 10.1016/j.jtbi.2013.07.004
URL : https://hal.archives-ouvertes.fr/hal-00821794

T. Smieszek and M. Salathé, A low-cost method to assess the epidemiological importance of individuals in controlling infectious disease outbreaks See related commentary article here http, BMC MEDICINE, vol.1136, issue.3511, pp.1741-7015, 2013.

N. Masuda and P. Holme, Predicting and controlling infectious disease epidemics using temporal networks, F1000Prime Reports, vol.5, 2013.
DOI : 10.12703/P5-6

V. Gemmetto, A. Barrat, and C. Cattuto, Mitigation of infectious disease at school: targeted class closure vs school closure, BMC Infectious Diseases, vol.9, issue.1, p.695, 2014.
DOI : 10.1371/journal.pone.0086028
URL : https://hal.archives-ouvertes.fr/hal-01238750

A. J. Conlan, Measuring social networks in British primary schools through scientific engagement, Proceedings of the Royal Society B: Biological Sciences, vol.40, issue.11, pp.1467-1475, 2011.
DOI : 10.1111/j.1440-1754.2004.00486.x

M. Granovetter, Network Sampling: Some First Steps, American Journal of Sociology, vol.81, issue.6, pp.1287-1303, 1976.
DOI : 10.1086/226224

O. Frank, Sampling and estimation in large social networks, Social Networks, vol.1, issue.1, pp.91-101, 19781979.
DOI : 10.1016/0378-8733(78)90015-1

G. Kossinets, Effects of missing data in social networks, Social Networks, vol.28, issue.3, pp.247-268, 2006.
DOI : 10.1016/j.socnet.2005.07.002

F. Viger, A. Barrat, L. Dall-'asta, C. Zhang, and E. Kolaczyk, What is the real size of a sampled network? The case of the Internet, Physical Review E, vol.75, issue.5, p.56111, 2007.
DOI : 10.1103/PhysRevE.75.056111
URL : https://hal.archives-ouvertes.fr/hal-00014555

C. A. Bliss, C. M. Danforth, and P. S. Dodds, Estimation of Global Network Statistics from Incomplete Data, PLoS ONE, vol.28, issue.10, p.108471, 2014.
DOI : 10.1371/journal.pone.0108471.s001

Y. Zhang, E. D. Kolaczyk, and B. D. Spencer, Estimating network degree distributions under sampling: An inverse problem, with applications to monitoring social media networks, The Annals of Applied Statistics, vol.9, issue.1, 2013.
DOI : 10.1214/14-AOAS800

G. Cimini, T. Squartini, A. Gabrielli, and D. Garlaschelli, Systemic risk analysis in reconstructed economic and financial networks. ArXiv e-prints, 2014.

M. Génois, Data on face-to-face contacts in an office building suggests a low-cost vaccination strategy based on community linkers. ArXiv e-prints, 2014.

J. Onnela, Structure and tie strengths in mobile communication networks, Proceedings of the National Academy of Sciences, vol.104, issue.18, pp.7332-7336, 2007.
DOI : 10.1073/pnas.0610245104

M. Karsai, Small but slow world: How network topology and burstiness slow down spreading, Physical Review E, vol.83, issue.2, p.25102, 2011.
DOI : 10.1103/PhysRevE.83.025102

S. Blower and M. Go, The importance of including dynamic social networks when modeling epidemics of airborne infections: does increasing complexity increase accuracy?, BMC Medicine, vol.411, issue.1, p.88, 2011.
DOI : 10.1038/35082140

R. Pfitzner, I. Scholtes, A. Garas, C. J. Tessone, and F. Schweitzer, Betweenness Preference: Quantifying Correlations in the Topological Dynamics of Temporal Networks, Physical Review Letters, vol.110, issue.19, 2013.
DOI : 10.1103/PhysRevLett.110.198701

L. Gauvin, A. Panisson, C. Cattuto, and A. Barrat, Activity clocks: spreading dynamics on temporal networks of human contact, Scientific Reports, vol.22, issue.5, 2013.
DOI : 10.1038/srep03099
URL : https://hal.archives-ouvertes.fr/hal-00836266

I. Scholtes, Causality-driven slow-down and speed-up of diffusion in non-Markovian temporal networks, Nature Communications, vol.37, p.5024, 2014.
DOI : 10.1038/ncomms6024

L. Gauvin, A. Panisson, A. Barrat, and C. Cattuto, Revealing latent factors of temporal networks for mesoscale intervention in epidemic spread. ArXiv e-prints, 2015.

L. Gauvin, A. Panisson, and C. Cattuto, Detecting the Community Structure and Activity Patterns of Temporal Networks: A Non-Negative Tensor Factorization Approach, PLoS ONE, vol.2008, issue.10, p.86028, 2014.
DOI : 10.1371/journal.pone.0086028.s013

E. Valdano, L. Ferreri, C. Poletto, and V. Colizza, Analytical computation of the epidemic threshold on temporal networks. ArXiv e-prints, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01275250

R. Pastor-satorras, C. Castellano, P. Van-mieghem, and A. Vespignani, Epidemic processes in complex networks. ArXiv e-prints, 2014.