Research & Publications

Research interests

  • Machine Learning
  • Data and Text Mining
  • Social and Diffusion Networks

Focus on problems

  • Data clustering and cluster analysis
  • Learning methods for high-dimensional data
  • Representation and clustering of text documents
  • Multimedia data semantics (text, image, video)
  • Online learning on data streams
  • Diffusion processes and control in networks


  • Multivariate Hawkes Processes for Large-scale Inference, R. Lemonnier, K. Scaman, A. Kalogeratos, to 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] – Prior arxiv version [arXiv link].

    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.

  • Suppressing Epidemics in Networks using Priority-Planning, K. Scaman, A. Kalogeratos, N. Vayatis, IEEE Transactions on Network Science and Engineering, vol. 3, no. 4, pp. 271-285, 2016. [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.

  • Dynamic control of social diffusions using extensions of the SIS model, A. Kalogeratos, S. Sarao, K. Scaman, and N. Vayatis, to appear as a full oral presentation in 2016 Conference on Complex Systems at Amsterdam (no proceedings). Abstract

    Diffusion processes model propagation phenomena on complex networks, such as epidemics, information diffusion, and viral marketing. In many situations, it is critical to suppress an undesired diffusion process by means of dynamic resource allocation, where one need to decide targeted actions by taking into account the evolving infection state of the network.

    In the context of continuous-time SIS, and with provided full information regarding the nodes’ state, we consider the scenario where a budget of treatment resources of limited efficiency is available at each time for distribution to infected nodes. Recent results on this particular problem include the Priority Planning approach which computes a linear ordering of the nodes with minimal maxcut, and the optimal greedy approach called Largest Reduction of Infectious Edges (LRIE). The latter is a simple, yet efficient, strategy that computes an intuitive priority score for the infected nodes which combines the notion of node virality (possibility to infect other nodes) and vulnerability (possibility to get reinfected after recovery).

    In this work we show that the principle of the LRIE score holds for a wide range of SIS-like modeling scenarios. More specifically, we propose the Generalized LRIE (gLRIE) strategy and study the dynamic diffusion control by introducing a two-fold extension to SIS which can model important aspects of social diffusion (e.g. behaviors or habits). The first considers nonlinear functions of infection rates with saturation. On the top that, our second extension considers competition in the sense that the two node states, the infected and the healthy, are both diffusive, though each node can only be in only one of them at a time. In this case, our control strategy has to align with the healthy diffusion to help it to win the competition. Finally, simulations on large-scale real and synthetic networks show the efficiency of gLIE.

  • Improving Text Stream Clustering using Term Burstiness and Co-burstiness, A. Kalogeratos, P. Zagorisios, and A. Likas, Hellenic Conference of Artificial Intelligence (SETN), 2016. [pdf][recompiled][notes][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.

  • Learning to Suppress SIS Epidemics in Networks, A. Kalogeratos, K. Scaman, and N. Vayatis, to appear in the NIPS 2015 Networks in the Social and Information Sciences workshop (NIPS 2015), 2015. [workshop link][pdf][poster][bib]. Abstract

    In this paper, we introduce the class of priority planning strategies for suppressing SIS epidemics taking place in a network. This is performed via dynamic allocation of treatment resources with limited efficiency to the infected nodes, according to a precomputed priority-order. Using recent theoretical results that highlight the role of the maxcut of a node ordering and the extinction time of an epidemic, we
    propose a simple and efficient strategy called MaxCut Minimization (MCM) that outperforms competing state-of-the-art strategies in simulated epidemic scenarios.

  • A Greedy Approach for Dynamic Control of Diffusion Processes in Networks, K. Scaman, A. Kalogeratos, and N. Vayatis, IEEE International Conference on Tools with Artificial Intelligence (ICTAI), pp. 652-659, 2015. [pdf][suppl. material][bib] .::||::. [software]. 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.

  • What Makes a Good Plan? An Efficient Planning Approach to Control Diffusion Processes in Networks, K. Scaman, A. Kalogeratos, and N. Vayatis, arXiv:1407.4760, 18 pages, 17 Jul 2014. [arXiv link][pdf][bib]. Abstract

    In this paper, we analyze the quality of a large class of simple dynamic resource allocation (DRA) strategies which we name priority planning. Their aim is to control an undesired diffusion process by distributing resources to the contagious nodes of the network according to a predefined priority-order. In our analysis, we reduce the DRA problem to the linear arrangement of the nodes of the network. Under this perspective, we shed light on the role of a fundamental characteristic of this arrangement, the maximum cutwidth, for assessing the quality of any priority planning strategy. Our theoretical analysis validates the role of the maximum cutwidth by deriving bounds for the extinction time of the diffusion process. Finally, using the results of our analysis, we propose a novel and efficient DRA strategy, called Maximum Cutwidth Minimization, that outperforms other competing strategies in our simulations.

  • Dynamic Treatment Allocation for Epidemic Control in Arbitrary Networks, K. Scaman, A. Kalogeratos, N. Vayatis, to appear in WSDM 2014 Diffusion in Networks and Cascade Analytics (DiffNet) Workshop, 2014. [workshop link][pdf][slides][poster][bib]. Abstract

    While static epidemic control, e.g. using vaccination, has been extensively studied for various network types, controlling epidemics dynamically remains an open issue. In this work, we first propose a general model formulation for the dynamic treatment allocation problem for the Susceptible-Infected-Susceptible diffusion model. Then, we investigate dynamic control strategies and further propose the novel Largest Reduction in Infectious Edges (LRIE) heuristic that gives priority to the treatment of nodes that have both a high dissemination rate of the infection to many healthy nodes,
    and low reinfection probability after recovery. Experiments on random and a real-world network show that the dynamic problem is significantly different from vaccination, since the latter strategies can lead to disastrous results, and that the proposed heuristic is an effective strategy under various initial infection conditions.

  • Mining Clinical Data, A. Kalogeratos, V. Chasanis, G. Rakocevic, A. Likas, Z. Babovic, M. Novakovic, Book: Computational Medicine in Data Mining and Modeling, Eds. G. Rakocevic et al., pp. 1-34, 2013. [link][pdf][bib]
  • Dip-means: an incremental clustering method for estimating the number of clusters, A. Kalogeratos and A. Likas, Proceedings of the 26th Annual Conference on Neural Information Processing Systems (NIPS), 2012. [pdf][poster][bib] .::||::. [software]. Abstract

    Learning the number of clusters is a key problem in data clustering. We present dip-means, a novel robust incremental method to learn the number of data clusters that can be used as a wrapper around any iterative clustering algorithm of k-means family. In contrast to many popular methods which make assumptions about the underlying cluster distributions, dip-means only assumes a fundamental cluster property: each cluster to admit a unimodal distribution. The proposed algorithm considers each cluster member as an individual ‘viewer’ and applies a univariate statistic hypothesis test for unimodality (dip-test) on the distribution of distances between the viewer and the cluster members. Important advantages are: i) the unimodality test is applied on univariate distance vectors, ii) it can be directly applied with kernel-based methods, since only the pairwise distances are involved in the computations. Experimental results on artificial and real datasets indicate the effectiveness of our method and its superiority over analogous approaches.

  • Text Document Clustering Using Global Term Context Vectors, A. Kalogeratos and A. Likas, Knowledge and Information Systems (KAIS), vol. 31, no. 3, pp. 455–474, 2012. [pdf][recompiled pdf][bib]. Abstract

    Despite the advantages of the traditional vector space model (VSM) representation, there are known deficiencies concerning the term independence assumption. The high dimensionality and sparsity of the text feature space and phenomena such as polysemy and synonymy can only be handled if a way is provided to measure term similarity. Many approaches have been proposed that map document vectors onto a new feature space where learning algorithms can achieve better solutions. This paper presents the global term context vector-VSM (GTCV-VSM) method for text document representation. It is an extension to VSM that: (i) it captures local contextual information for each term occurrence in the term sequences of documents; (ii) the local contexts for the occurrences of a term are combined to define the global context of that term; (iii) using the global context of all terms a proper semantic matrix is constructed; (iv) this matrix is further used to linearly map traditional VSM (Bag of Words—BOW) document vectors onto a ‘semantically smoothed’ feature space where problems such as text document clustering can be solved more efficiently. We present an experimental study demonstrating the improvement of clustering results when the proposed GTCV-VSM representation is used compared with traditional VSM-based approaches.

  • Document clustering using synthetic cluster prototypes, A. Kalogeratos and A. Likas, Data and Knowledge Engineering, vol.70, no. 3, pp. 284–306, 2011. [pdf][recompiled pdf][bib]. Abstract

    The use of centroids as prototypes for clustering text documents with the k-means family of methods is not always the best choice for representing text clusters due to the high dimensionality, sparsity, and low quality of text data. Especially for the cases where we seek clusters with small number of objects, the use of centroids may lead to poor solutions near the bad initial conditions. To overcome this problem, we propose the idea of synthetic cluster prototype that is computed by first selecting a subset of cluster objects (instances), then computing the representative of these objects and finally selecting important features. In this spirit, we introduce the MedoidKNN synthetic prototype that favors the representation of the dominant class in a cluster. These synthetic cluster prototypes are incorporated into the generic spherical k-means procedure leading to a robust clustering method called k-synthetic prototypes (k-sp). Comparative experimental evaluation demonstrates the robustness of the approach especially for small datasets and clusters overlapping in many dimensions and its superior performance against traditional and subspace clustering methods.

  • Movie Segmentation into Scenes and Chapters Using Locally Weighted Bag of Visual Words, V. Chasanis, A. Kalogeratos, and A. Likas, Proceedings of the ACM International Conference on Image and Video Retrieval (CIVR), pp. 8–10, 2009. [pdf][bib]. Abstract

    Movies segmentation into semantically correlated units is a quite tedious task due to ”semantic gap”. Low-level features do not provide useful information about the semantical correlation between shots and usually fail to detect scenes with constantly dynamic content. In the method we propose herein, local invariant descriptors are used to represent the key-frames of video shots and a visual vocabulary is created from these descriptors resulting to a visual words histogram representation (bag of visual words) for each shot. A key aspect of our method is that, based on an idea from text segmentation, the histograms of visual words corresponding to each shot are further smoothed temporally by taking into account the histograms of neighboring shots. In this way, valuable contextual information is preserved. The final scene and chapter boundaries are determined at the local maxima of the difference of successive smoothed histograms for low and high values of the smoothing parameter respectively. Numerical experiments indicate that our method provides high detection rates while preserving a good tradeoff between recall and precision.

  • A Significance-Based Graph Model for Clustering Web Documents, A. Kalogeratos and A. Likas, Hellenic Conference of Artificial Intelligence (SETN), LNAI 3955, pp. 516–519, Springer-Verlag, 2006. [pdf][extended pdf][bib]. Abstract

    Traditional document clustering techniques rely on single-term analysis, such as the widely used Vector Space Model. However, recent approaches have emerged that are based on Graph Models and provide a more detailed description of document properties. In this work we present a novel Significance-based Graph Model for Web documents that introduces a sophisticated graph weighting method, based on significance evaluation of graph elements. We also define an associated similarity measure based on the maximum common subgraph between the graphs of the corresponding web documents. Experimental results on artificial and real document collections using well-known clustering algorithms indicate the effectiveness of the proposed approach./p>

Note: Additional recompiled pdf versions are provided above with the material as published (very few typos were corrected, if existed).

Invited talks & presentations

  • 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 at the 42nd Middle European Cooperation in Statistical Physics (MECO), Feb 2017, Lyon, France.
  • 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).
  • 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.
  • 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].

PhD Thesis  

Received on April 2013, [page]

  • Knowledge Extraction Methods for Document Collections, Department of Computer Science, University of Ioannina [pdf][bib]
  • Presentation (April 17, 2013) [pdf]


DBPL record: [link] — Google scholar: [link]

Older staff from CS.UOI Lab

A last lab poster: [link]