H. Zang and J. C. Bolot, Mining call and mobility data to improve paging efficiency in cellular networks, Proceedings of the 13th annual ACM international conference on Mobile computing and networking , MobiCom '07, pp.123-134, 2007.
DOI : 10.1145/1287853.1287868

K. Y. Lai, Z. Tari, and P. Bertok, Supporting User Mobility through Cache Relocation, Mobile Information Systems, vol.1, issue.4, pp.275-307, 2005.
DOI : 10.1155/2005/513531

URL : http://doi.org/10.1155/2005/513531

U. Paul, A. P. Subramanian, M. M. Buddhikot, and S. R. Das, Understanding traffic dynamics in cellular data networks, 2011 Proceedings IEEE INFOCOM, pp.882-890, 2011.
DOI : 10.1109/INFCOM.2011.5935313

URL : http://www.wings.cs.sunysb.edu/~upaul/paper/Infocom11-final-version.pdf

Y. Zheng, L. Zhang, X. Xie, and W. Ma, Mining interesting locations and travel sequences from GPS trajectories, Proceedings of the 18th international conference on World wide web, WWW '09, pp.791-800, 2009.
DOI : 10.1145/1526709.1526816

URL : http://www2009.eprints.org/80/1/p791.pdf

M. C. González, C. A. Hidalgo, and A. Barabási, Understanding individual human mobility patterns, Nature, vol.89, issue.7196, pp.779-782, 2008.
DOI : 10.1038/nature06958

G. Ranjan, H. Zang, Z. Zhang, and J. Bolot, Are call detail records biased for sampling human mobility?, ACM SIGMOBILE Mobile Computing and Communications Review, vol.16, issue.3, pp.33-44, 2012.
DOI : 10.1145/2412096.2412101

URL : https://research.sprintlabs.com/publications/uploads/MC2R_2012_CDR_Bias_Mobility.pdf

C. Song, Z. Qu, N. Blumm, and A. Barabási, Limits of Predictability in Human Mobility, Limits of Predictability in Human Mobility, pp.1018-1021, 2010.
DOI : 10.1038/20144

C. Iovan, A. O. Raimond, T. Couronné, and Z. Smoreda, Moving and Calling: Mobile Phone Data Quality Measurements and Spatiotemporal Uncertainty in Human Mobility Studies, pp.247-265, 2013.
DOI : 10.1007/978-3-319-00615-4_14

M. Ficek and L. , Inter-Call Mobility model: A spatio-temporal refinement of Call Data Records using a Gaussian mixture model, 2012 Proceedings IEEE INFOCOM, pp.2012-469, 2012.
DOI : 10.1109/INFCOM.2012.6195786

D. Naboulsi, M. Fiore, S. Ribot, and R. Stanica, Large-Scale Mobile Traffic Analysis: A Survey, IEEE Communications Surveys & Tutorials, vol.18, issue.1, pp.99-2015
DOI : 10.1109/COMST.2015.2491361

URL : https://hal.archives-ouvertes.fr/hal-01132385

D. Zhang, J. Huang, Y. Li, F. Zhang, C. Xu et al., Exploring human mobility with multisource data at extremely large metropolitan scales, Proc. of MobiCom, 2014.
DOI : 10.1145/2639108.2639116

URL : http://www-users.cs.umn.edu/~tianhe/Papers/mobicom-zhang.pdf

G. Ranjan, H. Zang, Z. Zhang, and J. Bolot, Are call detail records biased for sampling human mobility?, ACM SIGMOBILE Mobile Computing and Communications Review, vol.16, issue.3, pp.33-44, 2012.
DOI : 10.1145/2412096.2412101

URL : https://research.sprintlabs.com/publications/uploads/MC2R_2012_CDR_Bias_Mobility.pdf

H. H. Jo, M. Karsai, J. Karikoski, and K. Kaski, Spatiotemporal correlations of handset-based service usages, EPJ Data Science, vol.27, issue.3, pp.1-18, 2012.
DOI : 10.1093/bioinformatics/btq675

S. Isaacman, R. Becker, R. Cáceres, S. Kobourov, M. Martonosi et al., Ranges of human mobility in Los Angeles and New York, 2011 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops), pp.88-93, 2011.
DOI : 10.1109/PERCOMW.2011.5766977

S. Hoteit, G. Chen, A. Viana, and M. Fiore, Filling the gaps, Proceedings of the Eleventh ACM Workshop on Challenged Networks, CHANTS '16, pp.45-50
DOI : 10.1109/WoWMoM.2016.7523554

URL : https://hal.archives-ouvertes.fr/hal-01448821

G. Chen, A. C. Viana, and C. Sarraute, Towards an adaptive completion of sparse Call Detail Records for mobility analysis, 2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), pp.2017-302
DOI : 10.1109/PERCOMW.2017.7917577

URL : https://hal.archives-ouvertes.fr/hal-01448822

A. Barabasi, The origin of bursts and heavy tails in human dynamics, Nature, vol.401, issue.7039, 2005.
DOI : 10.1038/44831

E. Chist-era, Mobile context-Adaptive CAching for COntent-centric networking (MACACO) project, https

N. Eagle and A. , Sandy) Pentland, Reality mining: Sensing complex social systems, Personal Ubiquitous Comput, pp.255-268, 2006.
DOI : 10.1007/s00779-005-0046-3

Y. Zheng, L. Zhang, X. Xie, and W. Ma, Mining interesting locations and travel sequences from GPS trajectories, Proceedings of the 18th international conference on World wide web, WWW '09, 2009.
DOI : 10.1145/1526709.1526816

URL : http://www2009.eprints.org/80/1/p791.pdf

S. Hoteit, S. Secci, S. Sobolevsky, C. Ratti, and G. Pujolle, Estimating human trajectories and hotspots through mobile phone data, Computer Networks, vol.64, pp.296-307, 2014.
DOI : 10.1016/j.comnet.2014.02.011

URL : https://hal.archives-ouvertes.fr/hal-01018885

S. Phithakkitnukoon, Z. Smoreda, and P. Olivier, Socio-Geography of Human Mobility: A Study Using Longitudinal Mobile Phone Data, PLoS ONE, vol.33, issue.6
DOI : 10.1371/journal.pone.0039253.s010

G. Khodabandelou, V. Gauthier, M. El-yacoubi, and M. Fiore, Population estimation from mobile network traffic metadata, 2016 IEEE 17th International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM), 2016.
DOI : 10.1109/WoWMoM.2016.7523554

URL : https://hal.archives-ouvertes.fr/hal-01426320

M. Ficek and L. , Inter-Call Mobility model: A spatio-temporal refinement of Call Data Records using a Gaussian mixture model, 2012 Proceedings IEEE INFOCOM, pp.469-477
DOI : 10.1109/INFCOM.2012.6195786

H. Jo, M. Karsai, J. Karikoski, and K. Kaski, Spatiotemporal correlations of handset-based service usages, EPJ Data Science, vol.27, issue.3, pp.1-18, 2012.
DOI : 10.1093/bioinformatics/btq675

URL : https://epjdatascience.springeropen.com/track/pdf/10.1140/epjds10?site=epjdatascience.springeropen.com

S. Isaacman, R. Becker, R. Cáceres, S. Kobourov, M. Martonosi et al., Identifying important places in people? lives from cellular network data, in: Pervasive computing, pp.133-151, 2011.

J. H. Friedman, Greedy function approximation: a gradient boosting machine, Annals of statistics, pp.1189-1232, 2001.