Large Graphs and Networks

Research on large graphs and networks is conducted by 11 professors and about 30 PhD students and postdocs.

Principal Investigators :

Pierre-Antoine Absil, Vincent Blondel, Olivier Bonaventure, Jean-Charles Delvenne, Yves Deville, Pierre Dupont, Julien Hendrickx, Raphaël Jungers, Yurii Nesterov, Etienne Rivière, Marco Saerens

Research Labs :

Machine Learning Group, IP Networking Lab, Cloud and Large Scale computing group

Research Areas :

We look at some of the most recent and fundamental computational challenges raised by large networks. We study questions related to the classification, equilibria calculation, visualization, hierarchical reduction, analysis of dynamical properties and stochastic analysis of large networks. We also develop new analysis techniques allowing to extract useful information from graphs and networks, for example by detecting tightly connected groups within the network, finding the most prestigious nodes, categorizing unlabeled nodes thanks to some labeled ones, computing similarities between nodes, etc.

Applications include topics such as data-mining of text documents, web-searching, analysis of telephone, traffic and electricity networks. The Internet, the largest deployed network today, is of particular interest. Measurement and modeling tools and techniques that we develop allow us to obtain more accurate information about its organization (interconnections between Internet Service Providers, network topologies, ...) and to build realistic models of computer networks. We are using these tools and models to better understand the structure of the Internet, and also to evaluate the performance of new networking protocols.

Most recent publications

Below are listed the 10 most recent journal articles and conference papers produced in this research area. You also can access all publications by following this link : see all publications.


Journal Articles


1. Nyéki, Anikó; Kerepesi, Csaba; Daroczy, Balint Zoltan; Benczúr, A; Milics, G; Nagy, J; Harsányi, E; Kovács, A.J.; Neményi, M. Application of spatio-temporal data in site-specific maize yield prediction with machine learning methods. In: Precision agriculture, (2021). doi:10.1007/s11119-021-09833-8 (Accepté/Sous presse). http://hdl.handle.net/2078.1/249754

2. Heroy, Samuel; Loaiza Saa, Isabella; Pentland, Alex; O’Clery, Neave. COVID-19 policy analysis: labour structure dictates lockdown mobility behaviour. In: Journal of The Royal Society Interface, Vol. 18, no.176 (2021). doi:10.1098/rsif.2020.1035. http://hdl.handle.net/2078.1/244781

3. Mehrmann, Volker; Van Dooren, Paul. Structured Backward Errors for Eigenvalues of Linear Port-Hamiltonian Descriptor Systems. In: SIAM Journal on Matrix Analysis and Applications, Vol. 42, no.1, p. 1-16 (2021). doi:10.1137/20m1344184. http://hdl.handle.net/2078.1/242981

4. Mehrmann, Volker; Van Dooren, Paul. Optimal Robustness of Port-Hamiltonian Systems. In: SIAM Journal on Matrix Analysis and Applications, Vol. 41, no.1, p. 134-151 (2020). doi:10.1137/19m1259092. http://hdl.handle.net/2078.1/242971

5. Mastronardi, Nicola; Van Dooren, Paul. On QZ steps with perfect shifts and computing the index of a differential-algebraic equation. In: IMA Journal of Numerical Analysis, (2020). doi:10.1093/imanum/draa049 (Accepté/Sous presse). http://hdl.handle.net/2078.1/242895

6. Laudadio, Teresa; Mastronardi, Nicola; Van Dooren, Paul. Computing the eigenvectors of nonsymmetric tridiagonal matrices. (Accepté/Sous presse). http://hdl.handle.net/2078.1/242881

7. Dopico, Froilán M.; Marcaida, Silvia; Quintana, María C.; Van Dooren, Paul. Local linearizations of rational matrices with application to rational approximations of nonlinear eigenvalue problems. In: Linear Algebra and its Applications, Vol. 604, p. 441-475 (2020). doi:10.1016/j.laa.2020.07.004. http://hdl.handle.net/2078.1/242857

8. Benner, Peter; Goyal, Pawan; Van Dooren, Paul. Identification of port-Hamiltonian systems from frequency response data. In: Systems & Control Letters, Vol. 143, p. 104741 (2020). doi:10.1016/j.sysconle.2020.104741. http://hdl.handle.net/2078.1/242854

9. Bankmann, Daniel; Mehrmann, Volker; Nesterov, Yurii; Van Dooren, Paul. Computation of the Analytic Center of the Solution Set of the Linear Matrix Inequality Arising in Continuous- and Discrete-Time Passivity Analysis. In: Vietnam Journal of Mathematics, Vol. 48, no.4, p. 633-659 (2020). doi:10.1007/s10013-020-00427-x. http://hdl.handle.net/2078.1/242849

10. Hendrickx, Julien. Julien M. Hendrickx [People in Control]. In: IEEE Control Systems Magazine, Vol. 40, no.4, p. 21-22 (2020). doi:10.1109/MCS.2020.2990512. http://hdl.handle.net/2078.1/235005


Conference Papers


1. Pinto, Samuel C; Andersson, Sean B; Hendrickx, Julien; Cassandras, Christos G. Optimal Minimax Mobile Sensor Scheduling Over a Network. 2021 xxx. http://hdl.handle.net/2078.1/249179

2. Legat, Antoine; Hendrickx, Julien. Local Network Identifiability with Partial Excitation and Measurement. In: Proceedings of the 57th IEEE Conference on Decision and Control (CDC 2020). p. 4342-4347 (2020). 2020 xxx. http://hdl.handle.net/2078.1/249174

3. Hendrickx, Julien; Rabbat, Michael G. Stability of Decentralized Gradient Descent in Open Multi-Agent Systems. In: Proceedings of the 57th IEEE Conference on Decision and Control (CDC 2020). p. 4885-4890 (2020). 2020 xxx. http://hdl.handle.net/2078.1/249172

4. Hendrickx, Julien; Olshevsky, Alex; Saligrama, Venkatesh. Minimax Rate for Learning From Pairwise Comparisons in the BTL Model. In: PMLR Proceedings of Machine Learning Research. Vol. 119, p. 4193-4202 (2020). 2020 xxx. http://hdl.handle.net/2078.1/249156

5. Shi, Mingming. Resilient asynchronous rendezvous of second-order agents under communication noise. 2020 xxx. http://hdl.handle.net/2078.1/238535

6. Shi,Ming; Feng,Shuai; Ishii,Hideaki. Quantized State Feedback Stabilization of Nonlinear Systems Under DoS. 2020 xxx. http://hdl.handle.net/2078.1/238534

7. Hendrickx, Julien; Olshevsky, Alex; Saligrama, venkatesh. MinimaxRank-1Factorization. In: Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS 2020). Vol. 108. 2020 xxx. http://hdl.handle.net/2078.1/225595

8. Bazanella, Alexandre S.; Gevers, Michel; Hendrickx, Julien. Network identification with partial excitation and measurement. In: 2019 IEEE 58th Conference on Decision and Control (CDC), IEEE, 2019, 9781728113982, p. 5500-5506 xxx. doi:10.1109/cdc40024.2019.9029909. http://hdl.handle.net/2078.1/249094

9. Bazanella, Alexandre S; Gevers, Michel; Hendrickx, Julien. Network Identification with Partial Excitation and Measurement. In: IEEE Conference on Decision and Control, Including the Symposium on Adaptive Processes. Proceedings. p. 5500-5506 (2019). I E E E, 2019 xxx. http://hdl.handle.net/2078.1/225593

10. Pinto, Samuel C; Andersson, Sean B.; Hendrickx, Julien; Cassandras, Christos G.. Optimal Multi-Agent Persistent Monitoring of the Uncertain State of a Finite Set of Targets. In: IEEE Conference on Decision and Control, Including the Symposium on Adaptive Processes. Proceedings. p. 4280-4285 (2019). In: 2019 IEEE 58th Conference on Decision and Control (CDC), I E E E, 2019 xxx. doi:10.1109/cdc40024.2019.9029521. http://hdl.handle.net/2078.1/225355