(see also my talks & conferences)

In progress…

  1. Dyck paths with a first return decomposition constrained by height submitted-preprint.pdf
    June 2017, Jean-luc Baril, Sergey Kirgizov and Armen Petrossian

  2. Forests and pattern avoiding permutations modulo pure descents submitted-preprint.pdf
    June 2017, Jean-luc Baril, Sergey Kirgizov and Armen Petrossian

  3. Patterns in treeshelves submitted-preprint.pdf
    Nov 2016, Jean-luc Baril, Sergey Kirgizov and Vincent Vajnovszki
    arXiv:1611.07793

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

Discrete mathematics

  1. The pure descent statistic on permutations .pdf
    June 2017, Accepted to Discrete Mathematics Jean-luc Baril, Sergey Kirgizov

  2. Suppression distance computation for hierarchical clusterings .hal
    François Queyroi and Sergey Kirgizov, Information Processing Letters 115.9 (2015): 689-693, 2015

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

Social network analysis

  1. Information fusion-based approach for studying influence on twitter using belief theory .pdf
    Lobna Azaza, Sergey Kirgizov, Marinette Savonnet, Éric Leclercq, Nicolas Gastineau, and Rim Faiz
    Computational Social Networks, 3 (2016), pp. 1–26

  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
    Tatiana Kondrashova, Alexander Frame, Sergey Kirgizov
    XXe Congrès de la SFSIC: Temps, temporalités et information-communication, 8-10 June 2016, Metz, France

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

  4. SNFreezer: a Platform for Harvesting and Storing Tweets in a Big Data Context
    chapter of the book Tweets from the Campaign Trail: Researching Candidates' Use of Twitter during the European Parliamentary Elections
    editor Peter Lang, to appear

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

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

Bioinformatics

  1. How much Proteins Contact Networks are eccentric? A signaling networks perspective of PCN eccentricity .pdf
    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
    editors Luisa Di Paola and Alessandro Giuliani, to appear, Elsevier, 2016+

Reinforcement Learning & Artificial Intelligence

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

PhD manuscript

  1. 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

Dynamics of Internet Topology: Measurements & Modelling

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

  2. Studying the impact of measurement frequency on the IP-level routing topology dynamics .pdf
    Sergey Kirgizov, Clémence Magnien, Fabien Tarissan and Azhu Liu, 24-ème édition du colloque Gretsi, 2013,

  3. Towards realistic modeling of IP-level routing topology dynamics .pdf
    Clémence Magnien, Amélie Medem, Sergey Kirgizov and Fabien Tarissan, Networking Science, December 2013, Volume 3, Issue 1-4, pp 24-33

Various short notes

  1. Metric space of hierarchies .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., 2013

  2. A new graph density (SJS article)
    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.

  3. Stochastic process estimation from partial observations: Poisson case .pdf
    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.