Large Graphs and Networks

Research on large graphs and networks is conducted by 12 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, Jean-Pierre Tignol

Research Labs :

Machine Learning Group, IP Networking Lab

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. Zamani, Moslem; Glineur, François; Hendrickx, Julien. On the Set of Possible Minimizers of a Sum of Convex Functions. In: IEEE Control Systems Letters, Vol. 8, p. 1871-1876 (2024). doi:10.1109/lcsys.2024.3414378. http://hdl.handle.net/2078.1/293048

2. Goujaud, Baptiste; Moucer, Céline; Glineur, François; Hendrickx, Julien; Taylor, Adrien B.; Dieuleveut, Aymeric. PEPit: computer-assisted worst-case analyses of first-order optimization methods in Python. In: Mathematical Programming Computation, Vol. 16, no.3, p. 337-367 (2024). doi:10.1007/s12532-024-00259-7. http://hdl.handle.net/2078.1/293047

3. Bousselmi, Nizar; Hendrickx, Julien; Glineur, François. Interpolation Conditions for Linear Operators and Applications to Performance Estimation Problems. In: SIAM Journal on Optimization, Vol. 34, no.3, p. 3033-3063 (2024). doi:10.1137/23m1575391. http://hdl.handle.net/2078.1/293045

4. Monnoyer de Galland de Carnières, Charles; Vizuete Haro, Renato Sebastian; Hendrickx, Julien; Panteley, Elena; Frasca, Paolo. Random Coordinate Descent for Resource Allocation in Open Multiagent Systems. In: IEEE Transactions on Automatic Control, Vol. 69, no.11, p. 7600-7613 (2024). doi:10.1109/tac.2024.3394349. http://hdl.handle.net/2078.1/292991

5. Houssiau, Florimond; Liénart, Thibaut; Hendrickx, Julien; de Montjoye, Yves-Alexandre. Web Privacy: A Formal Adversarial Model for Query Obfuscation. In: IEEE Transactions on Information Forensics and Security, Vol. 18, p. 2132-2143 (2023). doi:10.1109/tifs.2023.3262123. http://hdl.handle.net/2078.1/278124

6. Hendrickx, Julien; Gerencsér, Balázs. Trajectory convergence from coordinate-wise decrease of general energy functions. In: Automatica, Vol. 154, p. 111099 (2023). doi:10.1016/j.automatica.2023.111099. http://hdl.handle.net/2078.1/276070

7. Colla, Sébastien; Hendrickx, Julien. Automatic Performance Estimation for Decentralized Optimization. In: IEEE Transactions on Automatic Control, (2023). (Accepté/Sous presse). http://hdl.handle.net/2078.1/273038

8. Pinto, Samuel c.; Welikala, Shirantha; Andersson, Sean B.; Hendrickx, Julien; Cassandras, Christos G. Minimax Persistent Monitoring of a Network System. In: Automatica (Online), Vol. 149, p. 110808 (2022). doi:10.48550/arXiv.2201.06607. http://hdl.handle.net/2078.1/269250

9. Barbarino, Giovanni; Noferini, Vanni; Van Dooren, Paul. Role extraction for digraphs via neighborhood pattern similarity. In: Physical Review E, Vol. 106, no.5 (2022). doi:10.1103/physreve.106.054301. http://hdl.handle.net/2078.1/272680

10. Laudadio, Teresa; Mastronardi, Nicola; Van Dooren, Paul. Computing Gaussian quadrature rules with high relative accuracy. In: Numerical Algorithms, Vol. 92, no.1, p. 767-793 (2022). doi:10.1007/s11075-022-01297-9. http://hdl.handle.net/2078.1/272678

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