Cornet

In the context of team Cornet’s seminars, Sébastien De Valeriola (Free University of Brussels) will present his research work on “Fading Footprints in the Sand”: Analysis of Historical Networks and Robustness of Centrality Metrics, on February 10, 2023, at 11:35 in the room S6. Abstract: In this presentation, we consider centrality measures in their use in the analysis of historical networks. Since the sources used by historians to construct such networks are inherently incomplete and imperfect, it is necessary to take into account the robustness of these metrics as much as possible, i.e., their stability in the face of the vagaries that time has subjected historical documents to. To study this, we apply a battery of tests to three networks constructed from medieval history data. The first is a network of political history, representing the connections among the key figures in the conflict for the episcopal seat of Cambrai in the 11th century. The second is a network of socio-economic history, describing credit relationships among merchants in Ypres in the 13th century. The third is a hagiographical network depicting the connections between the lives of saints often compiled together in manuscripts. These tests are designed to simulate the processes of Plus d'infos

In the context of team Cornet’s seminars, Felipe Albuquerque (LIA/Espace) will present his research work on January 13, 2023, at 11:35 in the meeting room. Abstract: Frequently, social network information has been used to solve applications in Operation Research, such as the Team Formation Problem, whose goal is to find a subset of the workers that collectively cover a set of skills and can communicate effectively with each other. We use the Structural Balance Theory to define the compatibility between pairs of workers in the same team. For such, the social networks are represented by signed graphs, and the compatibility metric is calculated from the analysis of possible positive paths between pairs of distinct vertices. To solve this new version of the problem, we introduce an Integer Linear Programming formulation and a decomposition for it. We present an analysis of the performed computational tests that prove the potential efficiency of the decomposition proposed.

In the context of team Cornet’s seminars, Olivier Tsemogne (LIA) will present his research work on Game Theory for Cyber Deception against Network SIR Epidemics, on December 12, 2022, at 11:35 in the meeting room. Abstract: One of the deleterious aspects of the evolution of information and communication technologies is the combination of efficiency and effectiveness in the malware spread, which clearly constitutes a threat to the security of the users of these technologies. The term “user” here covers individuals, companies, governmental or non-governmental organizations, states, in short, any person or group of persons who communicate using the new technologies. Among these threats, we can cite rumors in a social network and the stealthy recruitment of naive users into a cyber terrorist army capable, for example, of causing serious damage to a company whose services are used by these same users. In these two cases, as in many others, users, tricked by skilled experts, participate against their will and against their own interest in a cyber attack of which they are not aware, the bearer of the attack being deception. Moreover, cybercriminals, unlike cyber defenders, violate the rules of privacy and are therefore the best, if not the only, informed Plus d'infos

In the context of team Cornet’s seminars, Olivier Bilenne (ENPC) will present his research work on Solutions of Poisson’s equation for first-policy improvement in parallel queueing systems, on November 18, 2022, at 11:35 in the meeting room. Abstract: This talk addresses the problem of (state-aware) job dispatching at minimum long-run average cost in a parallel queueing system with Poisson arrivals. Policy iteration is a technique for approaching optimality through improvement of an initial dispatching policy. Its implementation rests on the computation of value functions. In this context, we will consider the M/G/1-FCFS queue endowed with an arbitrary cost function for the waiting times of the incoming jobs. The associated relative value function is a solution of Poisson’s equation for Markov chains, which I propose to solve in the Laplace transform domain by considering an ancillary stochastic process extended to (imaginary) negative backlog states. This construction enables us to issue closed-form solutions for simple cost functions (polynomial, exponential, and their piecewise compositions), in turn permitting the derivation of interval bounds for the relative value functions to more general cost functions. Such bounds allow for an exact implementation of the first improvement step of policy iteration in a parallel queueing system.One objective Plus d'infos

In the context of team Cornet’s seminars, Omar Boufous (OrangeLabs) will present his research work on Learning a Correlated Equilibrium with Perturbed Regret Minimization, on October 22, 2021, at 11:35 in the meeting room. Abstract: We consider the problem of learning a correlated equilibrium of a finite non-cooperative game and show a new learning procedure, called Correlated Perturbed Regret Minimization (CPRM), for this purpose. CPRM uses a perturbed variant of a regret minimization to approach the set of correlated equilibrium distributions and a simple correlation device to stabilize the empirical probability distribution over action profiles. Numerical experiments provide evidence of the long run convergence of the realized sample paths to points in the set of approximate correlated equilibrium distributions. Additional simulation results suggest that CPRM is adaptive to changes in the game such as departures or arrivals of players.

In the context of team Cornet’s seminars, Rosa Figueiredo (LIA) will present her research work on Multiplicity in Signed Graph Partitioning, on June 6, 2022, at 11:35 in the meeting room. Abstract: In order to study real-world systems many works model them through signed graphs, i.e. graphs whose edges are labeled as either positive or negative. Such a graph is considered as structurally balanced when it can be partitioned into a number of modules, such that positive (negative) edges are located inside (in-between) the modules. When it is not the case, authors look for the closest partition to such balance, a problem called Correlation Clustering (CC). The standard approach used in the literature is to find a single partition and focus the rest of the analysis on it, as if it was sufficient to fully characterize the studied system. Yet, it may not reflect the structure of the network, and one may need to seek for other partitions to build a better picture. We study the space of optimal solutions of the CC. We propose an efficient enumeration method allowing to retrieve the complete space of optimal solutions of the CC. It combines an exhaustive enumeration strategy with neighborhoods of Plus d'infos