L. Hazard, 56 6.2.1 Hazard characteristics of random graphs, p.60

.. Application-to-bond-percolation, 64 6.4.1 Size and existence of the giant component, p.64

E. Application, 66 6.6.1 Subcritical behavior in the standard SIR model, p.69

.. Priority, 122 9.2.1 A healing plan to gradually remove a contagion, p.122

.. Experimental-results, 131 9.5.1 Setup and competitors Quality of the theoretical bound, p.132

M. Robustness-of, 135 9.6.1 Malicious modiication of the network, 137 9.6.3 Uncertainty in the localization of nodes in contact networks . . . . 137

.. Probabilistic-mapping-for-network-convergence, 153 10.2.3 The space of spaces, p.154

T. Gromov-wasserstein-distance and .. , 156 10.2.6 Examples of distances and mm-spaces, p.158

O. Spectral, 174 11.3.1 Convergence of spectrum, p.176

D. Adolphson and T. C. Hu, Optimal Linear Ordering, SIAM Journal on Applied Mathematics, vol.25, issue.3, pp.403-423, 1973.
DOI : 10.1137/0125042

A. Alfonsi and P. Blanc, Dynamic optimal execution in a mixed-market-impact Hawkes price model, Finance and Stochastics, vol.5, issue.10, pp.183-218, 2015.
DOI : 10.1007/s00780-015-0282-y

URL : https://hal.archives-ouvertes.fr/hal-00971369

S. Arora, E. Hazan, and S. Kale, Fast algorithms for approximate semideenite programming using the multiplicative weights update method, Proceedings of the 46th IEEE Symposium on Foundations of Computer Science, pp.339-348, 2005.

A. Barabási and R. Albert, Emergence of scaling in random networks, Science, vol.286, issue.5439, pp.509-512, 1999.

L. Bauwens and N. Hautsch, Modelling nancial high frequency data using point processes, 2009.
DOI : 10.1007/978-3-540-71297-8_41

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

E. Ben-naim and P. L. Krapivsky, Size of outbreaks near the epidemic threshold, Physical Review E, vol.34, issue.5, p.50901, 2004.
DOI : 10.1103/PhysRevE.66.016128

E. A. Bender and E. Canneld, The asymptotic number of labeled graphs with given degree sequences, Journal of Combinatorial Theory, Series A, vol.24, issue.3, pp.296-307, 1978.
DOI : 10.1016/0097-3165(78)90059-6

I. Benjamini and O. Schramm, Recurrence of distributional limits of nite planar graphs, Electronic Journal of Probability, vol.6, issue.23, pp.1-13, 2001.

B. Bollobás, The evolution of random graphs. Transactions of the, pp.257-274, 1984.

B. Bollobás, C. Borgs, J. Chayes, and O. Riordan, Percolation on dense graph sequences. The Annals of Probability, pp.150-183, 2010.

B. Bollobás, S. Janson, and O. Riordan, The phase transition in inhomogeneous random graphs, Random Structures and Algorithms, vol.123, issue.1, pp.3-122, 2007.
DOI : 10.1002/rsa.20168

C. Borgs, J. Chayes, A. Ganesh, and A. Saberi, How to distribute antidote to control epidemics, Random Structures and Algorithms, vol.115, issue.2, pp.204-222, 2010.
DOI : 10.1002/rsa.20315

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

C. Borgs, J. Chayes, L. Lovász, V. T. Sós, and K. Vesztergombi, Convergent sequences of dense graphs I: Subgraph frequencies, metric properties and testing, Advances in Mathematics, vol.219, issue.6, pp.1801-1851, 2008.
DOI : 10.1016/j.aim.2008.07.008

H. Brézis, Functional analysis, Sobolev spaces and partial diierential equations, 2011.

W. Chen, C. Wang, and Y. Wang, Scalable innuence maximization for prevalent viral marketing in large-scale social networks, Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.1029-1038, 2010.

W. Chen, Y. Wang, and S. Yang, EEcient innuence maximization in social networks, Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.199-208, 2009.

N. A. Christakis and J. H. Fowler, The Spread of Obesity in a Large Social Network over 32 Years, New England Journal of Medicine, vol.357, issue.4, pp.370-379, 2007.
DOI : 10.1056/NEJMsa066082

F. Chung, P. Horn, and A. Tsiatas, Distributing Antidote Using PageRank Vectors, Internet Mathematics, vol.6, issue.2, pp.237-254, 2009.
DOI : 10.1080/15427951.2009.10129184

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

F. R. Chung and P. D. Seymour, Graphs with small bandwidth and cutwidth, Discrete Mathematics, vol.75, issue.1-3, pp.113-119, 1989.
DOI : 10.1016/0012-365X(89)90083-6

URL : http://doi.org/10.1016/0012-365x(89)90083-6

R. Cohen, S. Havlin, and D. Ben-avraham, EEcient immunization strategies for computer networks and populations, Physical Review Letters, issue.24, p.91247901, 2003.

R. Crane and D. Sornette, Robust dynamic classes revealed by measuring the response function of a social system, Proceedings of the National Academy of Sciences, pp.15649-15653, 2008.
DOI : 10.1103/PhysRevLett.93.228701

J. Díaz, J. Petit, and M. Serna, A survey of graph layout problems, ACM Computing Surveys, vol.34, issue.3, pp.313-356, 2002.
DOI : 10.1145/568522.568523

M. Draief, A. Ganesh, and L. Massoulié, Thresholds for virus spread on networks, The Annals of Applied Probability, vol.18, issue.2, pp.359-378, 2008.
DOI : 10.1214/07-AAP470

URL : http://arxiv.org/abs/math/0606514

K. Drakopoulos, A. Ozdaglar, and J. N. , An Efficient Curing Policy for Epidemics on Graphs, IEEE Transactions on Network Science and Engineering, vol.1, issue.2, pp.67-75
DOI : 10.1109/TNSE.2015.2393291

K. Drakopoulos, A. Ozdaglar, and J. N. , Tsitsiklis 2014b. An eecient curing policy for epidemics on graphs. ArXiv e-prints, 1407, 22411.

N. Du, L. Song, M. Gomez-rodriguez, and H. Zha, Scalable innuence estimation in continuous-time diiusion networks, Advances in Neural Information Processing Systems, pp.3147-3155, 2013.

N. Du, L. Song, H. Woo, and H. Zha, Uncover topic-sensitive information diiusion networks, Proceedings of the 16th International Conference on Artiicial Intelligence and Statistics, pp.229-237, 2013.

G. Elek, Note on limits of finite graphs, Combinatorica, vol.15, issue.5, pp.503-507, 2007.
DOI : 10.1007/s00493-007-2214-8

G. Elek, Finite graphs and amenability, Journal of Functional Analysis, vol.263, issue.9, pp.2593-2614, 2012.
DOI : 10.1016/j.jfa.2012.08.021

URL : http://arxiv.org/abs/1204.0449

P. Erdös and A. Rényi, On the evolution of random graphs, Publications of the Mathematical Institute of the Hungarian Academy of Sciences, pp.17-61, 1960.

M. Farajtabar, N. Du, M. Gomez-rodriguez, I. Valera, H. Zha et al., Shaping social activity by incentivizing users, Advances in Neural Information Processing Systems, pp.2474-2482, 2014.

G. A. Forster and C. A. Gilligan, Optimizing the control of disease infestations at the landscape scale, Proceedings of the National Academy of Sciences, pp.4984-4989, 2007.
DOI : 10.1098/rsif.2005.0051

C. M. Fortuin, P. W. Kasteleyn, and J. Ginibre, Correlation inequalities on some partially ordered sets, Communications in Mathematical Physics, vol.26, issue.Suppl., pp.89-103, 1971.
DOI : 10.1007/BF01651330

N. Fournier and A. Guillin, On the rate of convergence in Wasserstein distance of the empirical measure. Probability Theory and Related Fields, pp.707-738, 2015.
URL : https://hal.archives-ouvertes.fr/hal-00915365

A. Ganesh, L. Massoulié, and D. Towsley, The eeect of network topology on the spread of epidemics, Proceedings of the 24th Conference of the IEEE Computer and Communications Societies, pp.1455-1466, 2005.

M. Gomez-rodriguez, D. Balduzzi, and B. Schölkopf, Uncovering the temporal dynamics of diiusion networks, Proceedings of the 28th International Conference on Machine Learning, pp.561-568, 2011.

M. Gomez-rodriguez, L. Song, H. Daneshmand, and B. Schoelkopf, Estimating diiusion networks: Recovery conditions, sample complexity & softthresholding algorithm, Journal of Machine Learning Research, 2015.

P. Grassberger, Critical percolation in high dimensions, Physical Review E, vol.53, issue.3, p.36101, 2003.
DOI : 10.1103/PhysRevE.67.036101

M. Gromov, Structures métriques pour les variétés riemanniennes, Textes mathématiques, 1981.

M. Gromov, Metric structures for Riemannian and non-Riemannian spaces, 1999.

L. H. Harper, Optimal Assignments of Numbers to Vertices, Journal of the Society for Industrial and Applied Mathematics, vol.12, issue.1, pp.131-135, 1964.
DOI : 10.1137/0112012

A. G. Hawkes and D. Oakes, A cluster process representation of a self-exciting process, Journal of Applied Probability, pp.493-503, 1974.

P. W. Holland, K. B. Laskey, and S. Leinhardt, Stochastic blockmodels: First steps, Social Networks, vol.5, issue.2, pp.109-137, 1983.
DOI : 10.1016/0378-8733(83)90021-7

R. Holley, Remarks on the FKG inequalities, Communications in Mathematical Physics, vol.22, issue.3, pp.227-231, 1974.
DOI : 10.1007/BF01645980

J. Horowitz and R. L. Karandikar, Mean rates of convergence of empirical measures in the Wasserstein metric, Journal of Computational and Applied Mathematics, vol.55, issue.3, pp.261-273, 1994.
DOI : 10.1016/0377-0427(94)90033-7

M. Juvan and B. Mohar, Optimal linear labelings and eigenvalues of graphs, Discrete Applied Mathematics, vol.36, issue.2, pp.153-168, 1992.
DOI : 10.1016/0166-218X(92)90229-4

M. Kaiser and C. C. Hilgetag, Spatial growth of real-world networks, Physical Review E, vol.60, issue.3, p.36103, 2004.
DOI : 10.1103/PhysRevE.69.036103

A. Kalogeratos, K. Scaman, and N. Vayatis, Learning to suppress SIS epidemics in networks, Networks in the Social and Information Sciences workshop, 2015.

D. Kempe, J. Kleinberg, and E. Tardos, Maximizing the spread of innuence through a social network, Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.137-146, 2003.

W. O. Kermack and A. G. Mckendrick, Contributions to the mathematical theory of epidemics. II. the problem of endemicity, Proceedings of the Royal society of London. Series A, issue.834, pp.13855-83, 1932.

M. Khouzani, S. Sarkar, and E. Altman, Optimal control of epidemic evolution, 2011 Proceedings IEEE INFOCOM, pp.1683-1691, 2011.
DOI : 10.1109/INFCOM.2011.5934963

P. Klepac, O. N. Bjørnstad, C. J. Metcalf, and B. T. , Optimizing Reactive Responses to Outbreaks of Immunizing Infections: Balancing Case Management and Vaccination, PLoS ONE, vol.7, issue.8, p.41428, 2012.
DOI : 10.1371/journal.pone.0041428.s003

E. D. Kolaczyk, Statistical analysis of network data : methods and models, Springer series in statistics, 2009.

R. Lemonnier, K. Scaman, and A. Kalogeratos, Multivariate Hawkes processes for large-scale inference. ArXiv e-prints, 1602, p.8418, 2016.

R. Lemonnier, K. Scaman, and N. Vayatis, Tight bounds for innuence in diiusion networks and application to bond percolation and epidemiology, Advances in Neural Information Processing Systems, pp.846-854, 2014.

R. Lemonnier, K. Scaman, and N. Vayatis, Spectral bounds in random graphs applied to spreading phenomena and percolation . ArXiv e-prints, 1603, p.7970, 2016.

R. Lemonnier and N. Vayatis, Nonparametric Markovian Learning of Triggering Kernels for Mutually Exciting and Mutually Inhibiting Multivariate Hawkes Processes, Machine Learning and Knowledge Discovery in Databases, pp.161-176, 2014.
DOI : 10.1007/978-3-662-44851-9_11

J. Leskovec, A. Krause, C. Guestrin, C. Faloutsos, J. Vanbriesen et al., Cost-eeective outbreak detection in networks, Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.420-429, 2007.

T. J. Liniger, Multivariate Hawkes processes, 2009.

L. Lovász, Large networks and graph limits, 2012.
DOI : 10.1090/coll/060

L. Lovász and B. Szegedy, Limits of dense graph sequences, Journal of Combinatorial Theory, Series B, vol.96, issue.6, pp.933-957, 2006.
DOI : 10.1016/j.jctb.2006.05.002

T. ?uczak, Component behavior near the critical point of the random graph process, Random Structures & Algorithms, vol.25, issue.3, pp.287-310, 1990.
DOI : 10.1002/rsa.3240010305

J. J. Mcauley and J. Leskovec, Learning to discover social circles in ego networks, Advances in Neural Information Processing Systems, pp.548-556, 2012.

F. Mémoli, Gromov???Wasserstein Distances and the Metric Approach to Object Matching, Foundations of Computational Mathematics, vol.13, issue.2, pp.417-487, 2011.
DOI : 10.1007/s10208-011-9093-5

F. Mémoli, The Gromov???Wasserstein Distance: A Brief Overview, Axioms, vol.119, issue.3, pp.335-341, 2014.
DOI : 10.1016/j.acha.2010.09.005

M. Molloy and B. Reed, A critical point for random graphs with a given degree sequence. Random structures & algorithms, pp.161-180, 1995.

M. Molloy and B. Reed, The Size of the Giant Component of a Random Graph with a Given Degree Sequence, Combinatorics, Probability and Computing, vol.7, issue.3, pp.295-305, 1998.
DOI : 10.1017/S0963548398003526

K. E. Nelson, Epidemiology of infectious disease: general principles. Infectious Disease Epidemiology Theory and Practice, pp.17-48, 2007.

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

M. E. Newman, Spread of epidemic disease on networks, Physical Review E, vol.38, issue.1, p.16128, 2002.
DOI : 10.1103/PhysRevE.66.016128

I. Norros and H. Reittu, On a conditionally Poissonian graph process, Advances in Applied Probability, vol.1, issue.01, pp.59-75, 2006.
DOI : 10.1002/rsa.20063

D. Oakes, The Markovian self-exciting process, Journal of Applied Probability, pp.69-77, 1975.

H. Otto-georgii, O. Häggström, and C. Maes, The random geometry of equilibrium phases, Phase Transitions and Critical Phenomena, vol.18, pp.1-142, 1999.
DOI : 10.1016/S1062-7901(01)80008-2

J. J. Pantrigo, R. Martí, A. Duarte, and E. G. Pardo, Scatter search for the cutwidth minimization problem, Annals of Operations Research, vol.29, issue.6, pp.285-304, 2012.
DOI : 10.1007/s10479-011-0907-2

E. G. Pardo, N. Mladenovi?, J. J. Pantrigo, and A. Duarte, Variable Formulation Search for the Cutwidth Minimization Problem, Applied Soft Computing, vol.13, issue.5, pp.2242-2252, 2013.
DOI : 10.1016/j.asoc.2013.01.016

R. Pastor-satorras, C. Castellano, P. Van-mieghem, and A. Vespignani, Epidemic processes in complex networks, Reviews of Modern Physics, vol.5550, issue.3, pp.925-979, 2015.
DOI : 10.1016/j.physleta.2007.01.094

M. Penrose, Random geometric graphs, 2003.
DOI : 10.1093/acprof:oso/9780198506263.001.0001

J. Pouget-abadie and T. Horel, Inferring Graphs from Cascades, Proceedings of the 24th International Conference on World Wide Web, WWW '15 Companion, pp.977-986, 2015.
DOI : 10.1145/2740908.2744107

B. A. Prakash, D. Chakrabarti, N. C. Valler, M. Faloutsos, and C. Faloutsos, Threshold conditions for arbitrary cascade models on arbitrary networks, Knowledge and Information Systems, vol.393, issue.3, pp.549-575, 2012.
DOI : 10.1007/s10115-012-0520-y

V. M. Preciado, M. Zargham, C. Enyioha, A. Jadbabaie, and G. Pappas, Optimal vaccine allocation to control epidemic outbreaks in arbitrary networks, 52nd IEEE Conference on Decision and Control, 2013.
DOI : 10.1109/CDC.2013.6761078

V. M. Preciado, M. Zargham, C. Enyioha, A. Jadbabaie, and G. J. Pappas, Optimal resource allocation for network protection: A geometric programming approach, 2013.

P. Reynaud-bouret, V. Rivoirard, F. Grammont, and C. , Tuleau-Malot 2014. Goodness-of--t tests and nonparametric adaptive estimation for spike train analysis, Journal of Mathematical Neurosciences, vol.4, issue.1, pp.1-41

G. Robins, P. Pattison, Y. Kalish, and D. Lusher, An introduction to exponential random graph (p*) models for social networks, Social Networks, vol.29, issue.2, pp.173-191, 2007.
DOI : 10.1016/j.socnet.2006.08.002

M. G. Rodriguez and B. Schölkopf, Innuence maximization in continuous time diiusion networks, Proceedings of the 29th International Conference on Machine Learning, pp.313-320, 2012.

E. Rodriguez-tello, J. Hao, and J. Torres-jimenez, An effective two-stage simulated annealing algorithm for the minimum linear arrangement problem, Computers & Operations Research, vol.35, issue.10, pp.3331-3346, 2008.
DOI : 10.1016/j.cor.2007.03.001

A. A. Saberi, Recent advances in percolation theory and its applications, Physics Reports, vol.578, pp.1-32, 2015.
DOI : 10.1016/j.physrep.2015.03.003

K. Scaman, A. Kalogeratos, and N. Vayatis, Dynamic treatment allocation for epidemic control in arbitrary networks, Diiusion Networks and Cascade Analytics workshop, 2014.

K. Scaman, A. Kalogeratos, and N. Vayatis, What makes a good plan? An eecient planning approach to control diiusion processes in networks. ArXiv e-prints, 1407, p.4760, 2014.

K. Scaman, A. Kalogeratos, and N. , Vayatis 2015a. A greedy approach for dynamic control of diiusion processes in networks, Proceedings of the 27th IEEE International Conference on Tools with Artiicial Intelligence, pp.652-659

K. Scaman, R. Lemonnier, and N. Vayatis, Anytime innuence bounds and the explosive behavior of continuous-time diiusion networks, Advances in Neural Information Processing Systems, pp.2026-2034, 2015.

C. M. Schneider, T. Mihaljev, S. Havlin, and H. J. Herrmann, Suppressing epidemics with a limited amount of immunization units On the geometry of metric measure spaces, Physical Review E Acta Mathematica, vol.84, issue.1961, pp.65-131, 2006.

K. Sturm, The space of spaces: curvature bounds and gradient ows on the space of metric measure spaces. ArXiv e-prints, pp.1208-0434, 2013.

H. Tong, B. A. Prakash, T. Eliassi-rad, M. Faloutsos, and C. Faloutsos, Gelling, and melting, large graphs by edge manipulation, Proceedings of the 21st ACM international conference on Information and knowledge management, CIKM '12, pp.245-254, 2012.
DOI : 10.1145/2396761.2396795

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

M. Trusov, R. E. Bucklin, and K. Pauwels, Effects of Word-of-Mouth Versus Traditional Marketing: Findings from an Internet Social Networking Site, Journal of Marketing, vol.73, issue.5, pp.90-102, 2009.
DOI : 10.1509/jmkg.73.5.90

J. Ugander, B. Karrer, L. Backstrom, and C. Marlow, The anatomy of the Facebook social graph. ArXiv e-prints, pp.1111-4503, 2011.

P. Van-mieghem, J. Omic, and R. Kooij, Virus Spread in Networks, IEEE/ACM Transactions on Networking, vol.17, issue.1, pp.1-14, 2009.
DOI : 10.1109/TNET.2008.925623

D. Vere-jones, Earthquake prediction - a statistician's view., Journal of Physics of the Earth, vol.26, issue.2, pp.129-146, 1978.
DOI : 10.4294/jpe1952.26.129

C. Villani, Optimal transport: old and new, Grundlehren der mathematischen Wissenschaften, 2009.
DOI : 10.1007/978-3-540-71050-9

Y. Wang, D. Chakrabarti, C. Wang, and C. Faloutsos, Epidemic spreading in real networks: an eigenvalue viewpoint, 22nd International Symposium on Reliable Distributed Systems, 2003. Proceedings., pp.25-34, 2003.
DOI : 10.1109/RELDIS.2003.1238052

D. J. Watts and S. H. Strogatz, Collective dynamics of 'small-world' networks, Nature, vol.393, issue.6684, pp.440-442, 1998.
DOI : 10.1038/30918

K. Zhou, H. Zha, and L. Song, Learning triggering kernels for multi-dimensional Hawkes processes, Proceedings of the 30th International Conference on Machine Learning, pp.1301-1309, 2013.