~~NOCACHE~~ /* DO NOT EDIT THIS FILE */ [[seminaires:distribue:index|Calcul distribué]]\\ Mardi 23 octobre 2018, 14 heures, Salle 3052\\ **Yllka Velaj** (CWI Amsterdam) //Stable Outcomes in Modified Fractional Hedonic Games// \\ In coalition formation games, self-organized coalitions are created as a result of the strategic interactions of independent agents. For each couple of agents $(i,j)$, weight $w_{i,j}=w_{j,i}$ reflects how much agents $i$ and $j$ benefit from belonging to the same coalition. We consider the modified fractional hedonic game, that is a coalition formation game in which agents' utilities are such that the total benefit of agent $i$ belonging to a coalition (given by the sum of $w_{i,j}$ over all other agents $j$ belonging to the same coalition) is averaged over all the other members of that coalition, i.e., excluding herself. Modified fractional hedonic games constitute a class of succinctly representable hedonic games. We are interested in the scenario in which agents, individually or jointly, choose to form a new coalition or to join an existing one, until a stable outcome is reached. To this aim, we consider common stability notions, leading to strong Nash stable outcomes, Nash stable outcomes or core stable outcomes: we study their existence, complexity and performance, both in the case of general weights and in the case of 0-1 weights. [[seminaires:distribue:index|Calcul distribué]]\\ Mardi 10 juillet 2018, 15 heures, Salle 3052\\ **Nicolas K. Blanchard** (IRIF) //Human-Computable Passwords// \\ Despite 25+ years of online experience and many innovations, passwords are still central to security, with all the risks and frustration they imply. Neither biometrics, nor password managers, nor systems like facebook connect can guarantee the security we require, and remembering dozens of different passwords becomes a usability nightmare. Following in the footsteps of Manuel Blum et al. we recently proposed a humanly-computable password scheme called Cue-Pin-Select. It can generate (and regenerate) passwords on the go using only the user's brain for computation. It has the advantage of creating nigh-unforgettable passwords, not requiring any external storage or computing device, and can be executed in less than a minute to create a new password (potentially down to 30s with a bit of training). All passwords generated are 52-bit secure and can maintain 40+ bit security even against adversaries in possession of a stolen password This talk will summarize work done in the past 6 months with Ted Selker and doesn't require any prior knowledge. It will start with the Cue-Pin-Select algorithm, cover an improvement we found that applies to all passphrase-based security systems, and explain some of the work currently being done to have better tools to study password schemes and human computation. The talk will be given in English unless the audience is entirely composed of French-speakers. [[seminaires:distribue:index|Calcul distribué]]\\ Jeudi 5 juillet 2018, 14 heures, Salle 3052\\ **Laurent Viennot** (Inria Paris et IRIF) //Introduction to the blockchain// \\ The collaborative construction of a blockchain is the core of Bitcoin protocol for allowing various participants to agree on a list of valid transactions. The talk will be very informal, the intent is to give a very basic introduction on how Bitcoin works and highlight some of the scientific challenges behind it. [[seminaires:distribue:index|Calcul distribué]]\\ Vendredi 4 mai 2018, 11 heures, Salle 3052\\ **Emanuele Natale** (Max-Planck-Institut für Informatik) //Pooling or Sampling: Collective Dynamics for Electrical Flow Estimation// \\ The computation of electrical flows is a crucial primitive for many recently proposed optimization algorithms on weighted networks. While typically implemented as a centralized subroutine, the ability to perform this task in a fully decentralized way is implicit in a number of biological systems. Thus, a natural question is whether this task can provably be accomplished in an efficient way by a network of agents executing a simple protocol. We provide a positive answer, proposing two distributed approaches to electrical flow computation on a weighted network: a deterministic process mimicking Jacobi's iterative method for solving linear systems, and a randomized token diffusion process, based on revisiting a classical random walk process on a graph with an absorbing node. We show that both processes converge to a solution of Kirchhoff's node potential equations, derive bounds on their convergence rates in terms of the weights of the network, and analyze their time and message complexity. [[seminaires:distribue:index|Calcul distribué]]\\ Mercredi 2 mai 2018, 14 heures, Salle 3052\\ **Mauro Sozio** (Telecom Paristech) //Fully Dynamic k-center Clustering and Graph Partitioning// \\ Recently, we have witnessed an explosion in the amount of data being readily available to us. This allows us to study and understand the world we live in with unprecedented details. However, the challenges of processing large amounts of data evolving over time are non-trivial. Our talk will start with discussing a few recent results in large-scale graph mining. In particular, we will briefly discuss the main ideas which allowed us to find dense subgraphs in graphs containing up to 20 billion edges. We will then continue our talk presenting a fully dynamic algorithm for k-center clustering, which is the problem of finding a partition of the input data points into k sets with minimum maximum radius. Most of the efforts in developing dynamic data mining algorithms have been focusing on the sliding window model (where at any given point in time only the most recent data items are retained) or more simplistic models. However, in many real-world applications one might need to deal with arbitrary deletions and insertions. For example, one might need to remove data items that are not necessarily the oldest ones, because they have been flagged as containing inappropriate content or due to privacy concerns. Clustering trajectory data might also require to deal with more general update operations. We develop a (2+epsilon)-approximation algorithm for the k-center clustering problem with "small" amortized cost under the fully dynamic adversarial model. In such a model, points can be added or removed arbitrarily, provided that the adversary does not have access to the random choices of our algorithm. The amortized cost of our algorithm is poly-logarithmic when the ratio between the maximum and minimum distance between any two points in input is bounded by a polynomial, while k and epsilon are constant. Our theoretical results are complemented with an extensive experimental evaluation on dynamic data from Twitter, Flickr, as well as trajectory data, demonstrating the effectiveness of our approach. We conclude our talk with some interesting directions for future work. In particular, we are going to discuss the challenges in developing a fully dynamic algorithm for graph partitioning. Seminaire du Pole [[seminaires:distribue:index|Calcul distribué]]\\ Jeudi 22 mars 2018, 14 heures, Salle 3052\\ **Przemyslaw Uznanski** (ETH Zurich) //Searching in the presence of errors, revisited// \\ In searching with errors, the Questioner has to locate a target among $n$ objects, by repeatedly asking queries to Responder. The possible queries are limited by the structure of the searched space, and depending on the error model, the answers are subject to either adversarial lies (bound in absolute number, or bound in rate $r \in [0,1]$), or to probabilistic errors with rate $p \in [0,1]$. Our interest lies in distance-based searching in graphs and in comparison based searching of integer ranges. In graph searching with vertex queries, whenever Questioner queries a vertex $v$, Responder answers with neighbor $w$ of $v$ that lies on the shortest path from $v$ to target or with $v$ itself (as introduced by Emamjomeh-Zadeh et al. [STOC 2016]). We show that a simple strategy of querying weighted-1-median vertex and multiplicatively updating weights of vertices leads to $O(\log n + L)$ queries with fixed $L$ lies, generalizing results of Emamjomeh-Zadeh et al. By careful selection of potential this leads to $O(\log n / \varepsilon^2)$ queries when number of lies is bounded linearly by ratio $r = 1/2-\varepsilon$, or are subject to errors with probability $p = 1/2-\varepsilon$ (in that case target is located correctly w.h.p.). This improves result of Emamjomeh-Zadeh et al. for some selection of parameters (locating w.h.p. when $\varepsilon = \Omega(1/\sqrt{\log n})$, and generalizes their result to other error models. We introduce an edge queries model, in which, whenever Questioner queries an edge $e$, Responder answers with endpoint of $e$ that is not further to target. We show that appropriately defining weighted-1-median edge leads to $O(\Delta(\log n + L))$ length strategy in graphs of max degree $\Delta$, which leads to $O(\Delta \log n / \varepsilon^2)$ queries when lies are bounded linearly with rate $r = \frac{1}{\Delta+1}(1-\varepsilon)$ and $O(\Delta \log^2 n/\varepsilon^2)$ with error probability $p = 1/2-\varepsilon$. Our results for edge queries almost automatically apply to comparison based search of integer ranges, simplifying and improving many existing results. We also note that vertex based queries translate to (not considered previously to our knowledge) ternary comparisons, and this shows a separation between power of binary and ternary comparisons in the linearly bounded lies (Questioner can find target iff $r < \frac13$ for former case, $r < \frac12$ for latter case). Tweaking initial distribution of potentials, our results apply to searching in unbounded integer domains as well, leading to both simplification and significant improvement in length of strategies, and providing first results for many combinations of query model and error model. [[seminaires:distribue:index|Calcul distribué]]\\ Mardi 20 mars 2018, 14 heures, Salle 1007\\ **Pierluigi Crescenzi** (Universite de Pise) //Computing node centrality in large graphs: from theory to practice and back// \\ The computation of several graph measures, based on the distance between nodes, is very often part of the analysis of real-world complex networks. The diameter, the betweenness and the closeness centrality, and the hyperbolicity are typical examples of such measures. In this talk, we will focus on the computation of one specific measure, that is, the node closeness centrality which is basically the inverse of the average distance of a node from all other nodes of the graph. Even though polynomial-time algorithms are available for the computation of this measure, in practice these algorithms are not useful, due to the huge size of the networks to be analysed. One first theoretical question is, hence, whether better algorithms can be designed, whose worst-case complexity is (almost) linear in the size of the input graph. We will first show that, unfortunately, for the closeness centrality no such algorithm exists (under reasonable complexity theory assumptions). This will lead us back to the practical point of view: we will then describe a heuristics that allows us to compute the above measure in (practical) linear time, even though its worst-case complexity is (in practice) intractable. This result will finally motivate our return to theory in order to understand the reason why, in practice, this heuristics works so well: we will indeed conclude by showing that, in the case of several random graph generating models, the average time complexity of the heuristics is indeed (almost) linear. This talk will summarise the research work that I have done in collaboration with Elisabetta Bergamini, Michele Borassi, Michel Habib, Andrea Marino, Henning Meyerhenke, and Luca Trevisan, and will be mostly based on two papers presented at ALENEX 2016, and SODA 2017. [[seminaires:distribue:index|Calcul distribué]]\\ Mardi 13 février 2018, 14 heures, Salle 1007\\ **Nicolas Behr** (ENS) //Combinatorics of stochastic rewriting systems// \\ Based on recent joint work with G.H.E. Duchamp and K.A. Penson (arXiv:1712.06575, on the combinatorics of chemical reaction systems) and with V. Danos and I. Garnier (in preparation), I will explain how dynamical and statistical aspects of stochastic graph rewriting systems may be computed using combinatorial techniques. The main part of the talk will focus on the special case of discrete graph rewriting (i.e. chemical reactions) for clarity and brevity, but time permitting the generalization to generic graph rewriting in the form of a stochastic mechanics formalism based on the concept of rule algebras will be discussed as well. [[seminaires:distribue:index|Calcul distribué]]\\ Mardi 16 janvier 2018, 14 heures, Salle 3014\\ **Nicolas Thiery** (Université Paris Sud) //Computing huge subspaces of diagonal harmonic polynomials: symmetries to the rescue!// \\ Last spring, I visited François Bergeron and we worked on his favorite objects: the spaces H(n,k) of diagonal harmonic polynomials in k sets of n variables. Those spaces connect to many areas of algebraic combinatorics, representation theory and beyond, and the case H(n,2) became famous a decade ago with the n! conjecture and its proof by Haiman. To fuel his ongoing studies François needed to compute the structure of H(5,6). This is a space of dimension 6.10^5 made of polynomials in 30 variables of degree up to 15, each having thousands of terms. In this talk, I'll explain how the calculation can now be completed in 45 minutes with a dozen cores and ~15Go of memory. This exploits a combination of strategies (symmetries, representation theory of the symmetric and general linear group, ...), each of which reduces the complexity in time and memory by one or two orders of magnitude. There will be little prerequisites and it's my hope that some strategies (and maybe the code!) could be used in other contexts.