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. Dopico, Froilán M.; Lawrence, Piers W.; Pérez, Javier; Van Dooren, Paul. Block Kronecker linearizations of matrix polynomials and their backward errors. In: Numerische Mathematik, (2018). doi:10.1007/s00211-018-0969-z. http://hdl.handle.net/2078.1/200798

2. Dopico, Froilán M.; Pérez, Javier; Van Dooren, Paul. Structured backward error analysis of linearized structured polynomial eigenvalue problems. In: Mathematics of Computation, , p. 1 (2018). doi:10.1090/mcom/3360. http://hdl.handle.net/2078.1/200697

3. Taylor, Adrien B.; Hendrickx, Julien; Glineur, François. Exact Worst-Case Convergence Rates of the Proximal Gradient Method for Composite Convex Minimization. In: Journal of Optimization Theory and Applications, Vol. 178, p. 455-476 (2018). doi:10.1007/s10957-018-1298-1. http://hdl.handle.net/2078.1/198401

4. Birpoutsoukis, Georgios; Csurcsia, Péter Zoltán; Schoukens, Johan. Efficient multidimensional regularization for Volterra series estimation. In: Mechanical Systems and Signal Processing, Vol. 104, p. 896-914 (2018). doi:10.1016/j.ymssp.2017.10.007. http://hdl.handle.net/2078.1/195999

5. Vlaar, Martijn P.; Birpoutsoukis, Georgios; Lataire, John; Schoukens, Maarten; Schouten, Alfred C.; Schoukens, Johan; van der Helm, Frans C. T. Modeling the Nonlinear Cortical Response in EEG Evoked by Wrist Joint Manipulation. In: IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 26, no.1, p. 205-215 (2018). doi:10.1109/tnsre.2017.2751650. http://hdl.handle.net/2078.1/195996

6. Śliwiński, Przemysław; Marconato, Anna; Wachel, Paweł; Birpoutsoukis, Georgios. Non-linear system modelling based on constrained Volterra series estimates. In: IET Control Theory & Applications, Vol. 11, no.15, p. 2623-2629 (2017). doi:10.1049/iet-cta.2016.1360. http://hdl.handle.net/2078.1/195998

7. Birpoutsoukis, Georgios; Marconato, Anna; Lataire, John; Schoukens, Johan. Regularized nonparametric Volterra kernel estimation. In: Automatica, Vol. 82, p. 324-327 (2017). doi:10.1016/j.automatica.2017.04.014. http://hdl.handle.net/2078.1/195994

8. Birpoutsoukis, Georgios; Zoltán Csurcsia, Péter; Schoukens, Johan. Nonparametric Volterra Series Estimate of the Cascaded Water Tanks Using Multidimensional Regularization. In: IFAC-PapersOnLine, Vol. 50, no.1, p. 476-481 (2017). doi:10.1016/j.ifacol.2017.08.091. http://hdl.handle.net/2078.1/195992

9. Dopico, F.M.; Pérez, Javier; Van Dooren, Paul. STRUCTURED BACKWARD ERROR ANALYSIS OF LINEARIZED STRUCTURED POLYNOMIAL EIGENVALUE PROBLEMS. (Accepté/Sous presse). http://hdl.handle.net/2078.1/195895

10. Bouagada, Djillali; Melchior, Samuel; Van Dooren, Paul. Calculating the H∞ norm of a fractional system given in state-space form. In: Applied Mathematics Letters, Vol. 79, p. 51-57 (2018). doi:10.1016/j.aml.2017.11.019. http://hdl.handle.net/2078.1/195873


Conference Papers


1. Dzyga, Michalina; Ferens, Robert; Gusev, Vladimir; Szykula, Marek. Attainable Values of Reset Thresholds. In: Leibniz International Proceedings in Informatics (LIPIcs). Vol. 83, no.40, p. 1-14 (2017). Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik: Dagstuhl, Germany, 2017. doi:10.4230/LIPIcs.MFCS.2017.40. http://hdl.handle.net/2078.1/196224

2. Mauroy, Alexandre; Hendrickx, Julien. Spectral Identification of Networks with Inputs. In: Proceedings of the 56th IEEE Conference on Decision and Control (CDC 2017), 2017, p. 469-474. http://hdl.handle.net/2078.1/192249

3. Bazanella, Alexandre; Gevers, Michel; Hendrickx, Julien; Parraga, Adriane. Identifiability of dynamical networks: which nodes need be measured?. In: Proceedings of the 56th IEEE Conference on Decision and Control (CDC 2017), 2017, p. 5870-5875. http://hdl.handle.net/2078.1/192245

4. Taylor, Adrien; Hendrickx, Julien; Glineur, François. Performance Estimation Toolbox (PESTO): automated worst-case analysis of first-order optimization methods. In: Proceedings of the 56th IEEE Conference on Decision and Control (CDC 2017), 2017, p. 1278-1283. doi:10.1109/CDC.2017.8263832. http://hdl.handle.net/2078.1/192237

5. Abdelrahim, Mahmoud; Hendrickx, Julien; Heemels, Maurice. MAX-consensus in open multi-agent systems with gossip interactions. In: Proceedings of the 56th IEEE Conference on Decision and Control (CDC 2017), 2017, p. 4753-4758. http://hdl.handle.net/2078.1/192227

6. Hendrickx, Julien; Martin, Samuel. Open Multi-Agent Systems: Gossiping with Random Arrivals and Departures. In: Proceedings of the 56th IEEE Conference on Decision and Control (CDC 2017), 2017, p. 763-768. http://hdl.handle.net/2078.1/192016

7. Massart, Estelle M.; Chevallier, Sylvain. Inductive Means and Sequences Applied to Online Classification of EEG. In: Lecture Notes in Computer Science : Geometric Science of Information, springer,cham, 2017, 978-3-319-68445-1, p. 763-770. doi:10.1007/978-3-319-68445-1_88. http://hdl.handle.net/2078.1/189747

8. Peel, Leto. Graph-based semi-supervised learning for relational networks. http://hdl.handle.net/2078.1/182929

9. Gusev, Vladimir; Pribavkina, Elena V.. On Synchronizing Colorings and the Eigenvectors of Digraphs. In: Leibniz International Proceedings in Informatics (LIPIcs) (Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik), Piotr Faliszewski and Anca Muscholl and Rolf Niedermeier: Dagstuhl, Germany, 2016, 978-3-95977-016-3, p. 48:1-48:14. doi:10.4230/LIPIcs.MFCS.2016.48. http://hdl.handle.net/2078.1/183000

10. Hendrickx, Julien; Martin, Samuel. Open Multi-Agent Systems : Gossiping with Deterministic Arrivals and Departures. doi:10.1109/ALLERTON.2016.7852357. http://hdl.handle.net/2078.1/179916