News

  • New students – We welcome three new students in the ML on Networks theme of MLMDA group, Mathilde Fekom (MSc thesis), Matteo Neri (MSc thesis), and Marin Scalbert (MSc internship), that are going to be with us at CMLA this semester.
  • Invited Talk – A two-hours seminar talk on “Epidemics, Competition and Resource Management” was given in Paris at a Working Group on Machine Learning and Big Data of the French Ministry of Social Affairs and Health (organizer: Magali Beffy). The group is formed by scientists and researchers working on various organizations/institutions (Ministry of Health, INSEE,  IRDES, Dauphine) – 25/1/2017.
  • MECO 2017 – Part of the ongoing work on behavioral epidemics and dynamic control done with Stefano Sarao, Kevin Scaman, and Nicolas Vayatis is going to be presented in a poster format by Stefano at the 42nd Middle European Cooperation in Statistical Physics (MECO), Feb 2017, Lyon, France.
  • ΑΑΑΙ 2017 Multivariate Hawkes Processes for Large-scale Inference“, co-authored with Rémi Lemonnier and Kevin Scaman, will appear in the 31st AAAI Conference on Artificial Intelligence (AAAI-17), San Francisco, CA, US. [638 papers accepted / 2,590 submissions : 24.63% rate!] – [pdf][supplementary][poster].
    Abstract

    In this paper, we present a framework for fitting multivariate Hawkes processes for large-scale problems both in the number of events in the observed history n and the number of event types d (i.e. dimensions). The proposed Low-Rank Hawkes Process (SLRHP) framework introduces a low-rank approximation of the kernel matrix that allows to perform the nonparametric learning of the d^2 triggering kernels using at most O(ndr^2) operations, where r is the rank of the approximation (r << d, n). This comes as a major improvement to the existing state-of-the-art inference algorithms that are in O(nd^2). Furthermore, the low-rank approximation allows SLRHP to learn representative patterns of interaction between event types, which is usually for the analysis of complex processes in real-world networks.

  • Jury member in PhD defense – Kevin Scaman successfully defended in October his PhD thesis at CMLA, ENS Cachan. I am glad that I have been co-supervisor and collaborator to Kevin for three years, and finally a jury member in his defense.
  • MORANE project – The project that I will be primarily been involved in the next year has been launched on Sep. 2016 and is in collaboration with SNCF, the French National Railway Company (read more).
  • CCS 2016 Part of our work with Stefano Sarao, Kevin Scaman, and Nicolas Vayatis, appeared at the Conference on Complex Systems 2016 at Amsterdam as a full oral presentation entitled “Dynamic control of social diffusions using extensions of the SIS model” (on a working paper).
  • PC Member – 2nd International Workshop on “Data Science for Social Media and Risk“, part of IEEE ICDM 2016.
  • Journal paper – “Suppressing Epidemics in Networks using Priority-Planning”, co-authored with K. Scaman, and N. Vayatis, was published in IEEE Transactions on Network Science and Engineering (TNSE) [pdf][bib]. Abstract

    In this paper, we analyze a large class of dynamic resource allocation (DRA) strategies, named priority planning, that aim to suppress SIS epidemics taking place in a network. This is performed by distributing treatments of limited efficiency to its infected nodes, according to a priority-order precomputed offline. Under this perspective, an efficient DRA strategy for a given network can be designed by learning a proper linear arrangement of its nodes. In our theoretical analysis, we derive upper and lower bounds for the extinction time of the diffusion process that reveal the role of the maxcut of the considered linear arrangement. Accordingly, we highlight that the cutwidth, which is the minimum maxcut of all possible linear arrangements for a network, is a fundamental network property that determines the resource budget required to suppress the epidemic under priority planning. Finally, by making direct use of our theoretical results, we propose a novel and efficient DRA strategy, called maxcut minimization (MCM), which outperforms other competing strategies in our simulations, while offering desirable robustness under various noise profiles.

  • Invited Talk – Dynamic suppression of epidemics on networks“, at the Complex Networks research group of LIP6, Paris 6 (Jussieu), France, 13/6/2016.
  • Invited Talk – Epidemics in the new socio-economic era: challenges and applications“, at the Department of Computer Science and Engineering, University of Ioannina, Greece, 25/5/2016.
  • Invited Talk – Suppressing epidemics on arbitrary networks using treatment resources of limited efficiency“, at INRA Research Center at Jouy-en-Josas, Paris area, 4/3/2016.
  • TechReport – A technical report under the title “Multivariate Hawkes Processes for Large-Scale Inference“, co-authored with R. Lemonnier and K. Scaman was released, Feb 26, 2016. [arXiv link][pdf][bib].
    Abstract

    In this paper, we present a framework for fitting multivariate Hawkes processes for large-scale problems both in the number of events in the observed history n and the number of event types d (i.e. dimensions). The proposed Low-Rank Hawkes Process (LRHP) framework introduces a low-rank approximation of the kernel matrix that allows to perform the nonparametric learning of the d^2 triggering kernels using at most O(ndr^2) operations, where r is the rank of the approximation (r << d, n). This comes as a major improvement to the existing state-of-the-art inference algorithms that are in O(nd^2). Furthermore, the low-rank approximation allows LRHP to learn representative patterns of interaction between event types, which may be valuable for the analysis of such complex processes in real world datasets. The effciency and scalability of our approach is illustrated with numerical experiments on simulated as well as real datasets.

  • SETN 2016 – Accepted paper “Improving Text Stream Clustering using Term Burstiness and Co-burstiness“, co-authored with P. Zagorisios and A. Likas, at the Hellenic Conference of Artificial Intelligence (SETN), May 18-20, 2016. [pdf][slides][bib]. Abstract

    In text streams, documents appear over time and their timestamps can be used to improve typical approaches for text representation and clustering. A way to exploit temporal information is through the detection of bursty terms in such streams, i.e. terms that appear in many documents during short time period. Research efforts so far have shown that utilizing the burst information in the text representation can improve the performance of text clustering algorithms. However, most attempts take into account the bursty terms individually, without investigating the relation between them.

    In this work, we take advantage of the fact that most of the important documents of a topic are published during the period in which the `main’ topic terms are bursty. Therefore, we focus on both term burstiness and co-burstiness by determining groups of terms that are simultaneously bursty at a time period and also co-occurring in the same documents. Next, the documents that contain co-bursty terms from those groups, are considered as important for the respective topics and are used to construct robust synthetic prototypes following an agglomerative process. These prototypes are finally used to initialize in a deterministic fashion the spherical k-means clustering algorithm. Experimental results validate empirically the quality of the solutions provided by the proposed approach which seems to efficiently overcome the initialization problem of spherical k-means.

  • NIPS Networks 2015 A workshop paper with the title “Learning to Suppress SIS Epidemics in Networks“, co-authored with K. Scaman and N. Vayatis, will appear in the “Networks in the Social and Information Sciences” workshop in conjunction with 29th Annual Conference on Neural Information Processing Systems (NIPS 2015), November 9-12, 2015. [pdf][poster][bib].
  • ICTAI 2015 A conference paper with the title A Greedy Approach for Dynamic Control of Diffusion Processes in Networks, co-authored with K. Scaman and N. Vayatis, will appear in the IEEE International Conference on Tools with Artificial Intelligence (ICTAI), pp. 652-659, November 9-12, 2015. [pdf] — You can find more about this methodology in the related page. Abstract

    This paper investigates the control of a diffusion process by utilizing real-time information. More specifically, we allow the network administrator to adjust the allocation of control resources, a set of treatments that increase the recovery rate of infected nodes, according to the evolution of the diffusion process. We first present a novel framework for describing a large class of dynamic control strategies. These strategies rely on sorting the nodes according to a priority score in order to treat more sensitive regions first. Then, we propose the Largest Reduction in Infectious Edges (LRIE) control strategy which is based on a greedy minimization of the cost associated to the undesired diffusion, and has the benefits of being efficient and easy to implement. Our simulations, which were conducted using a software package that we developed and made available to the community, show that the LRIE strategy substantially outperforms its competitors in a wide range of scenarios.

  • FET proposal – A research proposal has been submitted on Sep 2015 for evaluation and consideration for getting funded in the frame of E.C. Horizon 2020 – Research and Innovation Framework Program.
  • Invited Summit Talk Efficient algorithms for the suppression of diffusion processes on networks with application in epidemiology and marketing, summit: “Big data and public policies for the transportation” organized by the General Direction of Infrastructures, Transportation, and the General Commissioner on Durable Development of the Ministry of Ecology, Durable Development and Energy, along with ENS Cachan and PSE-School of Economics of Paris (15/10/2015) [agenda][presentation].