A. Barabási and M. Pósfai, Network science, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol.406, issue.6794
DOI : 10.1038/35019019

A. Barrat and C. Cattuto, Temporal Networks of Face-to-Face Human Interactions, pp.191-216, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00823067

V. Batagelj and S. Praprotnik, An algebraic approach to temporal network analysis based on temporal quantities. Social Netw, Analys. Mining, vol.628, issue.1, pp.1-2822, 2016.
DOI : 10.1007/s13278-016-0330-4
URL : http://arxiv.org/pdf/1505.01569

C. Berge, The Theory of Graphs and Its Applications, 1962.

S. Bhadra and A. Ferreira, Computing multicast trees in dynamic networks and the complexity of connected components in evolving graphs, Journal of Internet Services and Applications, vol.2, issue.1, pp.269-275, 2012.
DOI : 10.1002/net.3230070103

D. Vincent, A. Blondel, G. Decuyper, and . Krings, A survey of results on mobile phone datasets analysis, EPJ Data Science, vol.4, issue.1, p.10, 2015.

B. Blonder, T. W. Wey, A. Dornhaus, R. James, and A. Sih, Temporal dynamics and network analysis, Methods in Ecology and Evolution, vol.108, issue.6, pp.958-972, 2012.
DOI : 10.1007/978-1-4419-1216-9_6
URL : http://onlinelibrary.wiley.com/doi/10.1111/j.2041-210X.2012.00236.x/pdf

J. A. Bondy, Graph Theory With Applications, 1976.
DOI : 10.1007/978-1-349-03521-2

M. Binh, A. Bui-xuan, A. Ferreira, and . Jarry, Computing shortest, fastest, and foremost journeys in dynamic networks, Int. J. Found. Comput. Sci, vol.14, issue.2, pp.267-285, 2003.

R. Sulo, C. , and T. Berger-wolf, Temporal Scale of Dynamic Networks, pp.65-94, 2013.

A. Casteigts, P. Flocchini, B. Mans, and N. Santoro, Shortest, Fastest, and Foremost Broadcast in Dynamic Networks, International Journal of Foundations of Computer Science, vol.26, issue.04, pp.499-522, 2015.
DOI : 10.1109/COMST.2006.323440
URL : http://arxiv.org/pdf/1210.3277

A. Casteigts, P. Flocchini, W. Quattrociocchi, and N. Santoro, Timevarying graphs and dynamic networks, IJPEDS, vol.27, issue.5, pp.387-408, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00854287

V. Conan, J. Leguay, and T. Friedman, Characterizing pairwise intercontact patterns in delay tolerant networks, Proceedings of the 1st International Conference on Autonomic Computing and Communication Systems, Autonomics '07, pp.1-19, 2007.
DOI : 10.4108/icst.autonomics2007.2131
URL : https://hal.archives-ouvertes.fr/hal-01311788

M. Corneli, P. Latouche, and F. Rossi, Modelling time evolving interactions in networks through a non stationary extension of stochastic block models, Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015, ASONAM '15, pp.1590-1591
DOI : 10.1016/j.jtbi.2010.11.033
URL : https://hal.archives-ouvertes.fr/hal-01222325

M. Corneli, P. Latouche, and F. Rossi, Block modelling in dynamic networks with non-homogeneous poisson processes and exact icl. Social Network Analysis and Mining, p.55, 2016.
DOI : 10.1007/s13278-016-0368-3
URL : https://hal.archives-ouvertes.fr/hal-01468548

E. David and K. Jon, Networks, Crowds, and Markets: Reasoning About a Highly Connected World, 2010.

R. Diestel, . Graph, and . Theory, of Graduate texts in mathematics, 2012.

P. Bhagat-lal-dutta, E. Ezanno, and . Vergu, Characteristics of the spatio-temporal network of cattle movements in France over a 5-year period, Preventive Veterinary Medicine, vol.117, issue.1, pp.79-94, 2014.
DOI : 10.1016/j.prevetmed.2014.09.005

J. Flores and M. Romance, On eigenvector-like centralities for temporal networks: Discrete vs. continuous time scales, Journal of Computational and Applied Mathematics, vol.330, 2017.
DOI : 10.1016/j.cam.2017.05.019

N. Gaumont, C. Magnien, and M. Latapy, Finding remarkably dense sequences of contacts in link streams. Social Netw, Analys. Mining, vol.687, issue.1, pp.1-8714, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01390043

N. Gaumont, T. Viard, R. Fournier-s-'niehotta, Q. Wang, and M. Latapy, Analysis of the Temporal and Structural Features of Threads in a Mailing-List, pp.107-118, 2016.
DOI : 10.1007/978-3-319-30569-1_8
URL : https://hal.archives-ouvertes.fr/hal-01345821

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, pp.1-13, 2014.
DOI : 10.1371/journal.pone.0086028.s013

B. George and S. Kim, Spatio-temporal Networks: Modeling and Algorithms, 2013.
DOI : 10.1007/978-1-4614-4918-8

H. Luiz, V. A. Gomes, J. M. Almeida, F. D. Almeida, L. A. Castro et al., Quantifying social and opportunistic behavior in email networks, Advances in Complex Systems, pp.99-112, 2009.

L. Gulyás, G. Kampis, and R. O. Legendi, Elementary models of dynamic networks, The European Physical Journal Special Topics, vol.99, issue.6, pp.1311-1333, 2013.
DOI : 10.1073/pnas.082090499

R. Hamon, P. Borgnat, P. Flandrin, and C. Robardet, Duality between temporal networks and signals: Extraction of the temporal network structures, 1505.

C. R. Harshaw, R. A. Bridges, M. D. Iannacone, J. W. Reed, and J. R. Goodall, GraphPrints, Proceedings of the 11th Annual Cyber and Information Security Research Conference on, CISRC '16, pp.1-15, 2016.
DOI : 10.1093/bioinformatics/btl038

E. Hernández-orallo, J. C. Cano, C. T. Calafate, and P. Manzoni, New approaches for characterizing inter-contact times in opportunistic networks, Ad Hoc Networks Modeling and Performance Evaluation of Wireless Ad Hoc Networks, pp.160-172, 2016.
DOI : 10.1016/j.adhoc.2016.04.003

P. Holme, Modern temporal network theory: a colloquium, The European Physical Journal B, vol.446, issue.9, p.234, 2015.
DOI : 10.1007/978-1-4614-4918-8

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

Y. Hulovatyy, H. Chen, and T. Milenkovic, Exploring the structure and function of temporal networks with dynamic graphlets, Bioinformatics, vol.32, issue.15, p.2402, 2016.
DOI : 10.1093/bioinformatics/btw310

M. Karsai, M. Kivelä, R. K. Pan, K. Kaski, J. Kertész et al., Small but slow world: How network topology and burstiness slow down spreading, Physical Review E, vol.83, issue.2, p.25102, 2011.
DOI : 10.1126/science.1174562
URL : http://arxiv.org/pdf/1006.2125

V. Kostakos, Temporal graphs. Physica A: Statistical Mechanics and its Applications, pp.1007-1023, 2009.
DOI : 10.1016/j.physa.2008.11.021

L. Kovanen, M. Karsai, K. Kaski, J. Kertész, and J. Saramäki, Temporal motifs in time-dependent networks, Journal of Statistical Mechanics: Theory and Experiment, vol.2011, issue.11, pp.2011-11005, 2011.
DOI : 10.1088/1742-5468/2011/11/P11005

L. Kovanen, K. Kaski, J. Kertész, and J. Saramäki, Temporal motifs reveal homophily, gender-specific patterns and grouptalk in mobile communication networks, 2013.
DOI : 10.1073/pnas.1307941110
URL : http://www.pnas.org/content/110/45/18070.full.pdf

G. Krings, M. Karsai, S. Bernhardsson, V. D. Blondel, and J. Saramäki, Effects of time window size and placement on the structure of an aggregated communication network, EPJ Data Science, vol.106, issue.6, p.4, 2012.
DOI : 10.1073/pnas.0811113106

M. Latapy, C. Magnien, and N. D. Vecchio, Basic notions for the analysis of large two-mode networks, Social Networks, vol.30, issue.1, pp.31-48, 2008.
DOI : 10.1016/j.socnet.2007.04.006
URL : https://hal.archives-ouvertes.fr/hal-01146080

G. Laurent, J. Saramäki, and M. Karsai, From calls to communities: a model for time-varying social networks, The European Physical Journal B, vol.1, issue.11, p.301, 2015.
DOI : 10.1371/journal.pcbi.1001109
URL : https://hal.archives-ouvertes.fr/hal-01203177

Y. Léo, C. Crespelle, and E. Fleury, Non-altering time scales for aggregation of dynamic networks into series of graphs, Proceedings of the 11th ACM Conference on Emerging Networking Experiments and Technologies, CoNEXT 2015, pp.291-320, 2015.

J. Leskovec, L. Backstrom, R. Kumar, and A. Tomkins, Microscopic evolution of social networks, Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD 08, pp.462-470, 2008.
DOI : 10.1145/1401890.1401948

J. Leskovec, J. M. Kleinberg, and C. Faloutsos, Graph evolution, ACM Transactions on Knowledge Discovery from Data, vol.1, issue.1, 2007.
DOI : 10.1145/1217299.1217301

C. Magnien and F. Tarissan, Time Evolution of the Importance of Nodes in dynamic Networks, Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015, ASONAM '15, pp.1200-1207
DOI : 10.1073/pnas.122653799
URL : https://hal.archives-ouvertes.fr/hal-01199042

L. Martinet, C. Crespelle, and E. Fleury, Dynamic Contact Network Analysis in Hospital Wards, pp.241-249
DOI : 10.1007/978-3-319-05401-8_23
URL : https://hal.archives-ouvertes.fr/hal-01104058

N. Masuda and R. Lambiotte, A Guide to Temporal Networks, of Series on Complexity Science, 2016.
DOI : 10.1142/q0033

C. Matias and V. Miele, Statistical clustering of temporal networks through a dynamic stochastic block model, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.4, issue.4, 2016.
DOI : 10.1214/10-AOAS359
URL : https://hal.archives-ouvertes.fr/hal-01167837

O. Michail, An Introduction to Temporal Graphs: An Algorithmic Perspective, pp.308-343
DOI : 10.1007/978-3-319-24024-4_18
URL : http://arxiv.org/pdf/1503.00278

M. E. Newman, Clustering and preferential attachment in growing networks, Physical Review E, vol.64, issue.2, p.25102, 2001.
DOI : 10.1103/PhysRevE.64.016132

M. Newman, Networks: An Introduction, 2010.
DOI : 10.1093/acprof:oso/9780199206650.001.0001

V. Nicosia, J. Tang, M. Musolesi, G. Russo, C. Mascolo et al., Components in time-varying graphs, Chaos: An Interdisciplinary Journal of Nonlinear Science, vol.22, issue.2, p.23101, 2012.
DOI : 10.1007/s00779-005-0046-3
URL : http://arxiv.org/pdf/1106.2134

A. Paranjape, A. R. Benson, and J. Leskovec, Motifs in Temporal Networks, Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, WSDM '17, pp.601-610, 2017.
DOI : 10.1145/1871437.1871694
URL : http://arxiv.org/abs/1612.09259

A. Payen, L. Tabourier, M. Latapy-beniamino-murgante, S. Misra, G. Borruso et al., Impact of Temporal Features of Cattle Exchanges on the Size and Speed of Epidemic Outbreaks, Computational Science and Its Applications -ICCSA 2017 -17th International Conference Proceedings, Part II, volume 10405 of Lecture Notes in Computer Science, pp.84-97, 2017.
DOI : 10.1017/S095026880500467X
URL : https://hal.archives-ouvertes.fr/hal-01550334

O. Patrick, P. J. Perry, and . Wolfe, Point process modelling for directed interaction networks, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.75, issue.5, pp.821-849, 2013.

S. Praprotnik and V. Batagelj, Semirings for temporal network analysis. CoRR, abs, 1603.

B. Ribeiro, N. Perra, and A. Baronchelli, Quantifying the effect of temporal resolution on time-varying networks, Scientific Reports, vol.107, issue.1, p.2013
DOI : 10.1145/2254756.2254794

E. Luis, N. Rocha, and . Masuda, Random walk centrality for temporal networks, New Journal of Physics, vol.16, issue.6, p.63023, 2014.

P. Rozenshtein, N. Tatti, and A. Gionis, Finding Dynamic Dense Subgraphs, ACM Transactions on Knowledge Discovery from Data, vol.11, issue.3, pp.1-2730, 2017.
DOI : 10.1145/956750.956789

N. Santoro, W. Quattrociocchi, and P. Flocchini, Arnaud Casteigts, and Frédéric Amblard. Time-varying graphs and social network analysis: Temporal indicators and metrics, 3rd AISB Social Networks and Multiagent Systems Symposium (SNAMAS), pp.32-38, 2011.

J. Saramäki and P. Holme, Exploring temporal networks with greedy walks, The European Physical Journal B, vol.83, issue.12, p.334, 2015.
DOI : 10.1103/PhysRevE.83.045102

I. Scholtes, N. Wider, and A. Garas, Higher-order aggregate networks in the analysis of temporal networks: path structures and centralities, The European Physical Journal B, vol.40, issue.3, p.61, 2016.
DOI : 10.2307/3033543

J. Scott, Social Network Analysis, Sociology, vol.21, issue.2, 2017.
DOI : 10.1111/j.1467-954X.1973.tb00500.x

S. Sikdar, N. Ganguly, and A. Mukherjee, Time series analysis of temporal networks, The European Physical Journal B, vol.85, issue.1, 1344.
DOI : 10.1103/PhysRevE.85.066105

A. E. Sizemore and D. S. Bassett, Dynamic graph metrics: Tutorial, toolbox, and tale, NeuroImage, 2017.
DOI : 10.1016/j.neuroimage.2017.06.081
URL : https://doi.org/10.1016/j.neuroimage.2017.06.081

A. B. Tom and . Snijders, The statistical evaluation of social network dynamics, Sociological Methodology, vol.31, issue.1, pp.361-395, 2001.

A. B. Tom, G. G. Snijders, C. E. Van-de-bunt, and . Steglich, Introduction to stochastic actor-based models for network dynamics, Social Networks Dynamics of Social Networks, vol.32, issue.1, pp.44-60, 2010.

C. Stadtfeld and P. Block, Interactions, actors, and time: Dynamic network actor models for relational events. Sociological Science, 2017.
DOI : 10.15195/v4.a14

C. Stadtfeld, J. Hollway, and P. Block, Dynamic Network Actor Models: Investigating Coordination Ties through Time, Sociological Methodology, vol.4, issue.1, 2017.
DOI : 10.1007/978-3-642-17058-4

M. Starnini and A. Baronchelli, Random walks on temporal networks, Physical Review E, vol.85, issue.5, p.56115, 2012.
DOI : 10.1016/j.socnet.2004.11.009
URL : https://hal.archives-ouvertes.fr/hal-00678579

J. Sun, D. Tao, and C. Faloutsos, Beyond streams and graphs, Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '06, pp.374-383, 2006.
DOI : 10.1145/1150402.1150445

. Takaguchi, . Taro, Y. Yano, and Y. Yoshida, Coverage centralities for temporal networks, The European Physical Journal B, vol.58, issue.2, p.35, 2016.
DOI : 10.1080/01621459.1963.10500830
URL : https://link.springer.com/content/pdf/10.1140%2Fepjb%2Fe2016-60498-7.pdf

J. Tang, S. Scellato, M. Musolesi, C. Mascolo, and V. Latora, Small-world behavior in time-varying graphs, Physical Review E, vol.09, issue.5, p.55101, 2010.
DOI : 10.1103/PhysRevE.79.036114

J. Tang, M. Musolesi, C. Mascolo, and V. Latora, Characterising temporal distance and reachability in mobile and online social networks, ACM SIGCOMM Computer Communication Review, vol.40, issue.1, pp.118-124, 2010.
DOI : 10.1145/1672308.1672329

J. Tang, M. Musolesi, C. Mascolo, V. Latora, and V. Nicosia, Analysing information flows and key mediators through temporal centrality metrics, Proceedings of the 3rd Workshop on Social Network Systems, SNS '10, pp.1-3, 2010.
DOI : 10.1145/1852658.1852661
URL : http://www.cl.cam.ac.uk/Research/SRG/netos/papers/sns10.pdf

D. Taylor, S. A. Myers, A. Clauset, M. A. Porter, and P. J. Mucha, Eigenvector-Based Centrality Measures for Temporal Networks, Multiscale Modeling & Simulation, vol.15, issue.1, pp.537-574, 2017.
DOI : 10.1137/16M1066142

W. Hedley-thompson, P. Brantefors, and P. Fransson, From static to temporal network theory: Applications to functional brain connectivity, Network Neuroscience, vol.1, issue.2, pp.69-99, 2017.
DOI : 10.1038/nrneurol.2009.198

M. Shahadat-uddin, P. Mahendra, K. Chung, and L. Hossain, Topological analysis of longitudinal networks, HICSS, 2013.

S. Uddin, L. Hossain, and R. T. Wigand, New Direction in Degree Centrality Measure: Towards a Time-Variant Approach, International Journal of Information Technology & Decision Making, vol.97, issue.04, p.865, 2014.
DOI : 10.1016/j.socnet.2010.03.006

S. Uddin, A. Khan, and M. Piraveenan, A set of measures to quantify the dynamicity of longitudinal social networks, Complexity, vol.5, issue.6, pp.309-320, 2016.
DOI : 10.1038/nrg1272

T. Viard and M. Latapy, Identifying roles in an IP network with temporal and structural density, 2014 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), pp.801-806, 2014.
DOI : 10.1109/INFCOMW.2014.6849333
URL : https://hal.archives-ouvertes.fr/hal-01009382

T. Viard, M. Latapy, and C. Magnien, Revealing contact patterns among high-school students using maximal cliques in link streams, Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015, ASONAM '15, pp.1517-1522
DOI : 10.1109/INFCOMW.2014.6849333
URL : https://hal.archives-ouvertes.fr/hal-01208330

T. Viard, M. Latapy, and C. Magnien, Computing maximal cliques in link streams, Theoretical Computer Science, vol.609, pp.245-252, 2016.
DOI : 10.1016/j.tcs.2015.09.030
URL : https://hal.archives-ouvertes.fr/hal-01112627

S. Wasserman and K. Faust, Social network analysis: Methods and applications, 1994.
DOI : 10.1017/CBO9780511815478

K. Wehmuth, A. Ziviani, and E. Fleury, A unifying model for representing time-varying graphs, 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA), pp.1-10, 2015.
DOI : 10.1109/DSAA.2015.7344810
URL : https://hal.archives-ouvertes.fr/hal-00941622

D. B. West, Introduction to Graph Theory, 2000.

J. Whitbeck, M. Dias-de-amorim, V. Conan, and J. Guillaume, Temporal reachability graphs, Proceedings of the 18th annual international conference on Mobile computing and networking, Mobicom '12, pp.377-388, 2012.
DOI : 10.1145/2348543.2348589
URL : https://hal.archives-ouvertes.fr/hal-01273067

H. Wu, J. Cheng, S. Huang, Y. Ke, Y. Lu et al., Path problems in temporal graphs, Proc. VLDB Endow, pp.721-732, 2014.
DOI : 10.14778/2732939.2732945

K. S. Xu and A. O. Hero, Dynamic Stochastic Blockmodels: Statistical Models for Time-Evolving Networks, pp.201-210, 2013.
DOI : 10.1007/978-3-642-37210-0_22
URL : http://web.eecs.umich.edu/~hero/Preprints/Xu_SBP_2013.pdf

M. Zignani, S. Gaito, G. P. Rossi, X. Zhao, H. Zheng et al., Link and triadic closure delay: Temporal metrics for social network dynamics, Proceedings of the Eighth International Conference on Weblogs and Social Media, 2014.