Watercolor landscape, Yulia Kirgizova, 2015-2016

In progress… (2)

  1. Gray codes for Fibonacci q-decreasing words
    Jean-Luc Baril, Sergey Kirgizov and Vincent Vajnovszki
    .pdf arXiv:2010.09505 Submitted!

  2. Pattern statistics in faro words and permutations
    Jean-Luc Baril, Alexander Burstein et Sergey Kirgizov
    .pdf arXiv:2010.0670 Submitted!

International Journals (13)

  1. Vive les équations des motifs et les équations des statistiques !
    Pattern distributions in Dyck paths with a first return decomposition constrained by height .pdf arXiv:1911.03119
    Discrete Mathematics, Volume 343, Issue 9, September 2020
    Jean-Luc Baril, Richard Genestier and Sergey Kirgizov

  2. Bijections between directed animals, multisets and Grand-Dyck paths .pdf arXiv:1906.11870
    The Electronic Journal of Combinatorics Volume 27, Issue 2 (2020), Article P2.10
    Jean-Luc Baril, David Bevan and Sergey Kirgizov

  3. Motzkin paths with a restricted first return decomposition .pdf
    INTEGERS 19, September 2019
    Jean-Luc Baril, Sergey Kirgizov and Armen Petrossian

  4. Enumeration of Łukasiewicz paths modulo some patterns .pdf
    Discrete Mathematics, Volume 342, Issue 4, April 2019
    Jean-Luc Baril, Sergey Kirgizov and Armen Petrossian

  5. Descent distribution on Catalan words avoiding a pattern of length at most three .pdf arXiv:1803.06706
    Discrete Mathematics, Volume 341, Issue 9, September 2018
    Jean-Luc Baril, Sergey Kirgizov and Vincent Vajnovszki

  6. Dyck paths with a first return decomposition constrained by height .pdf
    Discrete Mathematics, Volume 341, Issue 6, June 2018
    Jean-Luc Baril, Sergey Kirgizov and Armen Petrossian

  7. Forests and pattern avoiding permutations modulo pure descents .pdf
    Pure Mathematics and Applications, Volume 27, Issue 1, 06 August 2018 Jean-Luc Baril, Sergey Kirgizov and Armen Petrossian

  8. Patterns in treeshelves .pdf arXiv:1611.07793
    Discrete Mathematics, Volume 340, Issue 12, 2017
    Jean-Luc Baril, Sergey Kirgizov and Vincent Vajnovszki

  9. The pure descent statistic on permutations .pdf
    Discrete Mathematics, Volume 340, Issue 10, October 2017
    Jean-Luc Baril, Sergey Kirgizov

  10. Information fusion-based approach for studying influence on twitter using belief theory .pdf
    Computational Social Networks, Volume 3, Issue 1, 2016
    Lobna Azaza, Sergey Kirgizov, Marinette Savonnet, Éric Leclercq, Nicolas Gastineau, and Rim Faiz

  11. The complexity of deciding whether a graph admits an orientation with fixed weak diameter . hal
    Discrete Mathematics & Theoretical Computer Science (DMTCS), Volume 17, Issue 3, 2016
    Julien Bensmail, Romaric Duvignau, Sergey Kirgizov

  12. Suppression distance computation for hierarchical clusterings .hal
    Information Processing Letters, Volume 115, Issue 9, 2015
    François Queyroi and Sergey Kirgizov

  13. Towards realistic modeling of IP-level routing topology dynamics .pdf
    Networking Science, Volume 3, Issue 1-4, December 2013
    Clémence Magnien, Amélie Medem, Sergey Kirgizov and Fabien Tarissan

Peer-reviewed International Conferences (9)

  1. Pattern distribution in faro words and permutations
    Permutations patterns 2020, June 30 and July 1
    Joint work with Jean-Luc Baril
    Look at a .pdf poster, a short video (5 min) and a two page abstract our our work.

  2. Temporal density of community structure
    MARAMI 2019, Dijon, 8 Novembre 2019. Slides
    together with Éric Leclercq

  3. Pattern avoiding permutations modulo pure descent
    Permutation Patterns Conference at Reykjavik University, Iceland, 2017
    Jean-Luc Baril, Sergey Kirgizov and Armen Petrossian

  4. Temporal density of complex networks and ego-community dynamics .pdf
    Annual Conference on Complex Systems (ECCS or CCS), Amsterdam, 19-22 September, 2016 Éric Leclercq, Sergey Kirgizov

  5. Towards a Twitter Observatory: A multi-paradigm framework for collecting, storing and analysing tweets preprint.pdf
    RCIS 2016, IEEE Tenth International Conference on Research Challenges in Information Science, Grenoble, France, 1-3 June 2016
    Ian Basaille, Sergey Kirgizov, Éric Leclercq, Marinette Savonnet, et Nadine Cullot

  6. A web application for event detection and exploratory data analysis for Twitter data .pdf

    Twitter at the European Elections 2014: International Perspectives on a Political Communication Tool, Dijon, 2015
    Sergey Kirgizov, Éric Leclercq, Marinette Savonnet, Alexander Frame, Ian Basaille-Gahite

  7. Influence Assessment in Twitter Multi-Relational Network .pdf
    Eleventh International IEEE Conference on Signal Image Technologies and Internet-Based System (SITIS), Bangkok, Thailand, 2015
    Lobna Azaza, Sergey Kirgizov, Marinette Savonnet, Éric Leclercq, Rim Faiz

  8. On the complexity of turning a graph into the analogue of a clique .pdf

    9th International colloquium on graph theory and combinatorics, June 30-July 4, 2014
    Julien Bensmail, Romaric Duvignau, Sergey Kirgizov

  9. (best paper award) Using Reinforcement Learning for Autonomic Resource Allocation in Clouds: towards a fully automated workflow .pdf
    Seventh International Conference on Autonomic and Autonomous Systems, ICAS 2011, pages 67-74, 2011
    Xavier Dutreilh, Sergey Kirgizov, Olga Melekhova, Jacques Malenfant, Nicolas Rivierre and Isis Truck

Book chapters (1)

  1. SNFreezer: a Platform for Harvesting and Storing Tweets in a Big Data Context
    Éric Leclercq, Marinette Savonnet, Thierry Grison, Sergey Kirgizov & Ian Basaille
    chapter of the book Tweets from the Campaign Trail: Researching Candidates' Use of Twitter during the European Parliamentary Elections
    ed Frame, A., Mercier, A., Brachotte, G., & Thimm, C.
    Bern, Switzerland: Peter Lang D. Retrieved Sep 29, 2017

French Journals (2)

  1. Évaluation de l’influence polarisée dans un réseau multi-relationnel : application à twitter .htm
    Document Numérique, Volume 20, Issue 1, 2017
    Lobna Azaza, Marinette Savonnet, Éric Leclercq, Sergey Kirgizov, and Rim Faiz

  2. Un observatoire pour la modélisation et l’analyse des réseaux multi-relationnels. Une application à l’étude du discours politique sur Twitter .htm
    Document Numérique, Volume 20, Issue 1, 2017
    Ian Basaille, Eric Leclercq, Marinette Savonnet, Nadine Cullot, Sergey Kirgizov, Thierry Grison, Elisabeth Gavignet

Peer-Reviewed French National Conferences (10)

  1. Packing coloring and subsets preserving path distance slides.pdf and abstract.pdf
    Les 18es Journées Graphes et Algorithmes, Paris, 16-18 Novembre, 2016
    Nicolas Gastineau, Benjamin Gras, Sergey Kirgizov, Mahmoud Omidvar

  2. (Re)constuire la temporalité d’un événement médiatique sur Twitter : une étude contrastive
    (Re)constructing the temporality of media events on Twitter: a contrastive study
    XXe Congrès de la SFSIC: Temps, temporalités et information-communication, Metz, France, 8-10 June 2016 Tatiana Kondrashova, Alexander Frame, Sergey Kirgizov

  3. Évaluation de l’influence dans un réseau multi-relationnel : le cas de Twitter
    INFORSID’2016, Le congrès INFORSID (INFormatique des ORganisations et Systèmes d’Information et de Décision), du 31 mai au 3 juin, à Grenoble, France, 2016
    Lobna Azaza, Sergey Kirgizov, Marinette Savonnet, Éric Leclercq, Rim Faiz

  4. A la recherche des mini-publics : un problème de communautés, de singularités et de sémantique .pdf
    16ème conférence francophone sur l’Extraction et la Gestion des Connaissances (EGC 2016): l’atelier Données participatives et sociales), Reims, France, Janvier 2016
    Éric Leclercq, Sergey Kirgizov and Maximilien Danisch

  5. Evaluation de l’influence sur Twitter: Application au projet “Twitter aux Elections Européennes 2014” .pdf
    _Journée d’étude Etudier le Web politique : Regards croisés, Lyon, 2015_
    Lobna Azaza, Sergey Kirgizov, Éric Leclercq, Marinette Savonnet, Alexander Frame

  6. Papersᵞ, Discussing board for scientific papers (pdf)
    Conference SO Data 3, Paris, France, 26 Mars 2015
    Sergey Kirgizov

  7. Internet Topology Dynamics: stochastic process estimation from partial observations Journée jointe des GDR ISIS et Phénix “Analyse et inférence pour les réseaux”), Paris, 2013
    Sergey Kirgizov and Clémence Magnien

  8. Distribution multimodale de la taille du sous-graphe des plus courts chemins dans un graphe aléatoire .pdf
    Journées Graphes et Algorithmes (JGA), Orsay, Novembre 2013
    Sergey Kirgizov

  9. Dynamique de la topologie de l’internet : impact de la fréquence de mesure sur les observations slides
    Studying the impact of measurement frequency on the IP-level routing topology dynamics paper
    24ème colloque Gretsi}, Brest, France, 2013
    Sergey Kirgizov, Clémence Magnien, Fabien Tarissan and Azhu Liu

  10. Vers une modélisation réaliste de la dynamique de la topologie de routage au niveau IP .pdf
    Journées Automnales 2012 ResCom, Paris, 2012
    Sergey Kirgizov, Amélie Medem, Clémence Magnien, Fabien Tarissan

Notes, essais (6)

  1. The limit of generalised Dempster-Shafer-Smets operator draft.pdf, 2016
    Sergey Kirgizov, Nicolas Gastineau, Lobna Azaza

  2. How much Proteins Contact Networks are eccentric? A signaling networks perspective of PCN eccentricity .pdf, 2016
    chapter of the book Applications of Complex Networks Analysis to Biological Systems — From Biomolecular Structure to Gene Regulation
    Gabriele Oliva, Sergey Kirgizov, and Luisa di Paola

  3. A new graph density (SJS article), 2015
    For a given graph G we propose the non-classical definition of its true density: ρ(G) = Mass(G)/Vol (G), where the Mass of the graph G is a total mass of its links and nodes, and Vol (G) is a size-like graph characteristic, defined as a function from all graphs to R ∪ ∞. We show how the graph density ρ can be applied to evaluate communities, i.e “dense” clusters of nodes.

  4. Peaks and valleys in the size distribution of shortest path subgraphs .pdf , 2014
    Sergey Kirgizov and Clémence Magnien, preprint

  5. Metric space of hierarchies, 2013 .pdf
    We explain how to turn a set of all hierarchical clusterings of a graph into a metric space, using classical Hausdorff and Levenshtein distances.

  6. Stochastic process estimation from partial observations: Poisson case .pdf, 2014
    Sergey Kirgizov, François Queyroi
    Having a sequence of values v_0, v_{1∆} , v_{2∆} , . . . , v_{N∆}, which are measured every ∆ units of time, usually we are interested in the prediction of the future outcome of this sequence at time (N + 1)∆. But in some real-world cases we want to know, not the future, but rather the truth about the present: if Maria performs more observations per unit of time than Maximilian, how can he estimates the Maria’s results from his own? In this small note we consider the situation when the underlying process is Poissonian.

PhD manuscript

Empirical analysis and modeling of the Internet topology dynamics .pdf, 2014

Rapporteurs
Paulo GONÇALVES    Chargé de recherche, ENS Lyon, INRIA
André-Luc BEYLOT   Professeur, IRIT/ENSEEIHT
Examinateurs
Jeremie LEGUAY     Docteur, Thales Communications & Security
Stefano SECCI      Maître de Conférences, UPMC
Benoit DONNET      Professeur, Université de Liège
Directrice
Clémence MAGNIEN   Directrice de recherche, UPMC, CNRS