Éditeur(s) : EUTIC : le réseau international et interdisciplinaire pour les Enjeux et Usages des Technologies de l'Information et de la Communication EUTIC : le réseau international et interdisciplinaire pour les Enjeux et Usages des Technologies de l'Information et de la Communication
Extrait de : "Les écosystèmes numériques et la démocratisation informationnelle" : colloque, les 3 et 4 novembre 2015. Université des Antilles
Description : L'émergence des nouveaux moyens de communications tels que les blogs, les journaux en ligne ou les réseaux sociaux nous permettent d'aller plus loin dans la compréhension des comportements humains. En effet, ces espaces d'échange publiques sont aujourd'hui fermement ancrés dans nos sociétés modernes et s'avèrent être de puissants capteurs des comportements sociaux et des mouvements d'opinions. Dans ce travail, nous nous intéressons à la diffusion d'informations et cherchons à comprendre quelles sont les conditions dans lesquelles une personne décide de s'exprimer sur un sujet. Nous proposons pour cela un ensemble de mesures qui visent à caractériser les comportements de diffusion. Nos mesures ont été utilisées sur les messages liés à deux événements ayant eu lieu au mois de Janvier 2015: la présentation par Microsoft d'un nouveau casque de réalité virtuelle et l'élection d'un parti politique de gauche radical en Grèce.
Siècle(s) traité(s) : 21
Droits : CC-BY-NC-ND - Attribution - Pas d'utilisation commerciale - Pas de modification
Permalien : http://www.manioc.org/fichiers/V15413
V15413
V15413

Éditeur(s) : HAL CCSD
Résumé : 6 pages, 4 figures. In ICML 2013 Workshop on Machine Learning for Bioacoustics
International audience
The work presented in this paper is part of a global framework which long term goal is to design a wireless sensor network able to support the observation of a population of endangered birds. We present the first stage for which we have conducted a knowledge discovery approach on a sample of acoustical data. We use MFCC features extracted from bird songs and we exploit two knowledge discovery techniques. One that relies on clustering-based approaches, that highlights the homogeneity in the songs of the species. The other, based on predictive modeling, that demonstrates the good performances of various machine learning techniques for the identification process. The knowledge elicited provides promising results to consider a widespread study and to elicit guidelines for designing a first version of the automatic approach for data collection based on acoustic sensors.
ICML 2013 Workshop on Machine Learning for Bioacoustics
Atlanta, United States

hal-00840741
https://hal.archives-ouvertes.fr/hal-00840741
ARXIV : 1306.5349

Éditeur(s) : HAL CCSD RNTI
Résumé : International audience
De nombreuses méthodes ont été proposées pour extraire des clus-ters des réseaux sociaux. Si un travail important est aujourd’hui mené sur laconception de méthodes innovantes capables de rechercher des clusters de na-ture différente, la plupart des approches font l’hypothèse de réseaux statiques.L’une des récentes méthodes concerne notamment la recherche de liens concep-tuels. Il s’agit d’une nouvelle approche de clustering de liens, qui exploite à lafois la structure du réseau et les attributs des noeuds dans le but d’identifier desliens fréquents entre des groupes de noeuds au sein desquels les noeuds par-tagent des attributs communs. Dans ce travail, nous nous intéressons au suivides liens conceptuels dans des réseaux dynamiques, c’est-à-dire des réseaux quiconnaissent des changements structurels importants. Nous cherchons en parti-culier à comprendre comment les liens conceptuels se forment et évoluent aucours du développement du réseau. Pour ce faire, nous proposons un ensemblede mesures qui visent à capturer des comportements caractérisant l’évolutionde ces clusters. Notre approche est ainsi utilisée pour comprendre l’évolutiondes liens conceptuels extraits sur deux réseaux réels : un réseau de co-auteursd’articles scientifiques et un réseau de communications mobiles. Les résultatsobtenus permettent de mettre en lumière des tendances significatives dans l’évo-lution des clusters sur ces deux réseaux.
Extraction et Gestion des Connaissances (EGC) 2017. Revue des Nouvelles Technologies de l'Information vol.RNTI-E-33
Grenoble, France

hal-01514245
https://hal.archives-ouvertes.fr/hal-01514245

Éditeur(s) : HAL CCSD
Résumé : International audience
Structure and evolution of networks have been areas of growing interest in recent years, especially with the emergence of Social Network Analysis (SNA) and its application in numerous fields. Researches on diffusion are focusing on network modeling for studying spreading phenomena. While the impact of network properties on spreading is now widely studied, involvement of network dynamicity is very little known. In this paper, we address the epidemiology context and study the consequences of network evolutions on spread of diseases. Experiments are conducted by comparing incidence curves obtained by evolution strategies applied on two generated and two real networks. Results are then analyzed by investigating network properties and discussed in order to explain how network evolution influences the spread. We present the MIDEN framework, an approach to measure impact of basic changes in network structure, and DynSpread, a 2D simulation tool designed to replay infections scenarios on evolving networks.
Database and Expert Systems Applications - 22nd International Conference, DEXA 2011, Toulouse, France, August 29 - September 2, 2011, Proceedings, Part II
Toulouse, France

hal-00614925
https://hal.archives-ouvertes.fr/hal-00614925
DOI : 10.1007/978-3-642-23091-2_49

Éditeur(s) : HAL CCSD
Résumé : International audience
EmailPrintRequest PermissionsThe emergence of new communication means such as online newspapers and exchange and sharing websites allow us to go further in the understanding of diffusion phenomena. Indeed, these public discussion areas are now firmly established in our societies and are known to be strong sensors of both human behaviors and collective feelings. In this paper we focus on diffusion phenomena that occur on Twitter and we propose a set of measures that aims to characterize globally and locally the processes. Our objective is to identify what are the conditions in which a person decides to forward the information. Our measures have been used to study two events occurred in January 2015: the presentation of Microsoft HoloLens, a new augmented reality headset, and the political election in Greece. The results obtained show a strong heterogeneity in the individual behaviours involved in the diffusion process. Our approach has been implemented into a graphical tool which is able to conduct real time analysis on any kind of topics occurring on Twitter.
IEEE International Conference on Research Challenges in Information Science (RCIS)
Athens, Greece

hal-01312382
https://hal.univ-antilles.fr/hal-01312382
DOI : 10.1109/RCIS.2015.7128894

Éditeur(s) : HAL CCSD Hermann-Éditions
Résumé : International audience
Dans le domaine des réseaux sociaux, la recherche de motifs fréquents correspond à l'extraction de sous-graphes retrouvés fréquemment dans un réseau ou un ensemble de réseaux. Contrairement aux approches classiques qui s'appuient uniquement sur des régularités structurelles, nous présentons dans ce travail l'outil GT-FLMin, capable d'exploiter les informations sur la structure pour extraire des régularités dans les liens qui connectent des noeuds similaires du point de vue de leurs caractéristiques. Nous appelons de tels motifs des liens fréquents.
Extraction et gestion des connaissances
Bordeaux, France

hal-00768426
https://hal.archives-ouvertes.fr/hal-00768426

Éditeur(s) : HAL CCSD
Résumé : International audience
One of the most common tasks in the area of social network mining is the extraction of frequent patterns from social networks. Although traditional approaches have been mainly focused on subgraphs occurring frequently in a network or a set of networks, new approaches have attempted to exploit network structure and node properties in order to elicit new kinds of patterns. One of these news approaches is the extraction of conceptual links, a solution that combines both structure and node properties for providing knowledge on the groups of nodes the most connected in a social network. However, if the extraction of conceptual links offers a great potential in terms of knowledge discovery as well as network visualization, the search for these kinds of patterns remains a computationally intensive problem. Thus, the efficiency of the extraction processes highly depends on the efficiency of the underlying algorithm. In this paper, we focus on the extraction of maximal frequent conceptual links (MFCL) and we propose a hybrid algorithm that applies a filter on the nodes for reducing the search space to the most frequent groups of nodes. Although our solution potentially causes loss of some patterns, we demonstrate its efficiency by comparison with the MFCLMin algorithm. We investigate the potential loss of patterns, the gain on the runtime, and the gain on the number of comparisons.
IEEE International Conference on Research Challenges in Information Science
Paris, France

hal-00841699
https://hal.archives-ouvertes.fr/hal-00841699

Éditeur(s) : HAL CCSD
Résumé : International audience
More and more animal species are endangered. In order to study and protect them, several measures have been taken, which now seem to show their limit. This work focuses on the counting process, a key issue for any project that aims to protect animals. Indeed, this paper proposes an algorithm for automatic counting of singing birds in their habitat by using wireless sensors fitted with microphone. Sensors are used to record audio samples of bird songs. These samples are used to extract some kinds of song fingerprint that are thereafter used to recognize the bird species, through a classification process. Afterwards, by analyzing data which have been collected by a central base, the network structure derived from detection fields of sensors, allows our algorithm to propose an estimate of singing birds in the habitat. This paper details the counting algorithm which uses graph theory and audio inputs comparison. Finally, we demonstrate our scheme efficiency through experimentations.
Local Computer Networks (LCN)
Clearwater, United States

hal-00786165
https://hal.archives-ouvertes.fr/hal-00786165
DOI : 10.1109/LCNW.2012.6424060

Éditeur(s) : HAL CCSD
Résumé : International audience
In the area of the link mining, frequent pattern discovery tasks generally consist in the search for subgraphs frequently found in a network or a set of networks. Very recently, new axes of this problem has been proposed through the search for frequent links. Unlike traditional approaches that focus solely on structural regularities, frequent link mining methods exploit both the network structure and the attributes of nodes for extracting regularities in the links existing between node groups that share common characteristics. However, extracting frequent links is still a particularly challenging and computationally intensive issue since it is much dependent on the number of links. In this study, we propose a solution that is able to reduce the computing time by reducing the search to only a subset of of nodes. Experiments were conducted to understand the effects of different thresholds of the subset size on the loss of patterns and the gain in terms of computation time. Our solution proves to be efficient for a rather wide range of thresholds.
Research Challenges in Information Science
Vallence, Spain

hal-00767039
https://hal.archives-ouvertes.fr/hal-00767039
DOI : 10.1109/RCIS.2012.6240432

Éditeur(s) : HAL CCSD
Résumé : International audience
In this article, we analyse a collaborative network to understand the underlying patterns that structure the co-writing process of scientific articles. Our goal is to identify and understand the collaboration tendencies from authors publishing activities. For this purpose, we adopt a descriptive modelling through a network approach that consists first in generating the collaborative network from data on publications. Nodes of the network are then enriched with a set of individual attributes extracted from the publishing activity of each author. Finally, we search for conceptual views, a recent link clustering approach, which allows to summarize any kind of networks by highlighting the sets of attributes found frequently linked. Results show that it exists strong tendencies that unconsciously structure the collaboration behaviours. In this paper, we present these tendencies and show how they evolve according to different extraction thresholds.
EEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Sydney, Australia

hal-01637563
https://hal.archives-ouvertes.fr/hal-01637563
DOI : 10.1145/3110025.3110146

Éditeur(s) : HAL CCSD
Résumé : 12 pages, in French. Colloque international et interdisciplinaire pour les enjeux et usages des technologies de l'information et de la communication, 2015 (EUTIC 2015)
International audience
The emergence of new communication media such as blogs, online newspapers and social networks allow us to go further in the understanding of human behavior. Indeed, these public exchange spaces are now firmly planted in our modern society and appear to be powerful sensors of social behavior and opinion movements. In this paper, we focus on information spreading and attempt to understand what are the conditions in which a person decides to speak on a subject. For this purpose, we propose a set of measures that aim to characterize the diffusion behavior. Our measures have been used on messages related to two events that took place in January 2015: presentation by Microsoft of a new virtual reality headset and the election of a political party of radical left in Greece.
Colloque international et interdisciplinaire pour les enjeux et usages des technologies de l'information et de la communication, 2015 (EUTIC 2015)
Schoelcher, France

hal-01514258
https://hal.archives-ouvertes.fr/hal-01514258
ARXIV : 1602.00252

Éditeur(s) : HAL CCSD IGI Global
Résumé : International audience
Graph Theoretic Approaches for Analyzing Large-Scale Social Networks

hal-01637538
https://hal.archives-ouvertes.fr/hal-01637538

Éditeur(s) : HAL CCSD IGI Global
Résumé : International audience
Standard data mining techniques have been applied and adapted for eliciting knowledge from social networks, by achieving classical tasks such as classification, search for frequent patterns or link prediction. Most works have exploited only the network topological structure, and therefore cannot be used to answer questions involving nodes features. For instance, the frequent pattern discovery task generally refers to the search for sub-networks frequently found in a single network or in a set of networks. In the same area, this paper focuses on the concept of frequent link that stands as a regularity found in a network on links between node groups that share common characteristics. The extraction of such links from a social network is a particularly challenging and computationally intensive problem, since it is much dependent on the number of links and attributes. In this study, we propose a solution for reducing the search space of frequent links, by filtering the nodes features on a criterion of frequency. We make the assumption that frequent links occur between sets of features that are themselves frequent. This property is used to reduce the search space and speed up the extraction process. We empirically show that it is well founded. We discuss the efficiency of the solution in terms of computation time and number of frequent patterns found depending on several frequency thresholds.
ISSN: 1947-8186

hal-00786166
https://hal.archives-ouvertes.fr/hal-00786166

Éditeur(s) : HAL CCSD
Résumé : International audience
Traditional network generation models aim to replicate global structural properties observed on various real-world networks (power law degree distribution, short average distance, community structure, etc.) through synthetic link formation mechanisms such as triadic closure or preferential attachment. Nowadays the large amount of data available allows to study more precisely the link formation processes and to compare the models to real situations. In this work, we analyse the communication activities of the users of a mobile telephony operator for studying the link formation process. The communications emitted and received by users are strong indicators for understanding the underlying mechanisms of the link formation process and highlighting how user attributes correlate with the formation process in such a network. In a first study conducted at a global level we show that the traditional mechanisms commonly used in network generation models are not the main link formation mechanisms involved in this network. In a second study, we analyse more precisely the link formation process by searching for correlations between some user attributes and the formation of the new links. These results provide valuable guidelines to the design of more realistic generation models taking into account network structure and node attributes.
IEEE International Conference on Research Challenges in Information Science (RCIS)
Marrakesh, Morocco

hal-01312314
https://hal.archives-ouvertes.fr/hal-01312314
DOI : 10.1109/RCIS.2014.6861065

Éditeur(s) : HAL CCSD
Résumé : The concept of network provides a model for representing a wide variety of objects and systems, both natural and social, in which a set of homogeneous or heterogeneous entities interact. It is now widely used to describe various kinds of relational structures. However, if everyone has an idea of the concept of network, we often ignore the implications that these structures have in real world phenomena. This is for example the case of processes such as the spread of a rumor, the disease transmission, or even the emergence of subjects of common interest for a group of individuals, in which the relations maintained between individuals, and their nature, often prove to be the main factors determining the evolution of the phenomenon. This is the reason why the study of networks has become one of the emerging areas in the 21st century called the "Science of networks." In this thesis, we address three issues of the domain of the science of networks: the problem of diffusion in social networks, where we have addressed more particularly the impact of the network dynamics on the diffusion process, the problem of the analysis of social networks, in which we have proposed a solution to take full advantage of all information available on the network by combining information on both structure and node attributes and the central problem of the social data collection, for which we have focused on the particular case of the data collection in a wild environment.
Le concept de réseau offre un modèle de représentation pour une grande variété d'objets et de systèmes, aussi bien naturels que sociaux, dans lesquels un ensemble d'entités homogènes ou hétérogènes interagissent entre elles. Il est aujourd'hui employé couramment pour désigner divers types de structures relationnelles. Pourtant, si chacun a une idée plus ou moins précise de ce qu'est un réseau, nous ignorons encore souvent les implications qu'ont ces structures dans de nombreux phénomènes du monde qui nous entoure. C'est par exemple le cas de processus tels que la diffusion d'une rumeur, la transmission d'une maladie, ou même l'émergence de sujets d'intérêt commun à un groupe d'individus, dans lesquels les relations que maintiennent les individus entre eux et leur nature s'avèrent souvent être les principaux facteurs déterminants l'évolution du phénomène. C'est ainsi que l'étude des réseaux est devenue l'un des domaines émergents du 21e siècle appelé la "Science des réseaux". Dans ce mémoire, nous abordons trois problèmes de la science des réseaux: le problème de la diffusion dans les réseaux sociaux, où nous nous sommes intéressés plus particulièrement à l'impact de la dynamique du réseau sur le processus de diffusion, le problème de l'analyse des réseaux sociaux, dans lequel nous avons proposé une solution pour tirer parti de l'ensemble des informations disponibles en combinant les informations sur la structure du réseau et les attributs des noeuds et le problème central de la collecte de données sociales, où nous nous sommes intéressés au cas particulier de la collecte de données en milieux sauvages.
https://tel.archives-ouvertes.fr/tel-00830882

Droits : info:eu-repo/semantics/OpenAccess
tel-00830882
https://tel.archives-ouvertes.fr/tel-00830882
https://tel.archives-ouvertes.fr/tel-00830882/document
https://tel.archives-ouvertes.fr/tel-00830882/file/These_STATTNER_2012.pdf

Éditeur(s) : HAL CCSD
Résumé : International audience
La recherche de liens conceptuels fréquents (FCL) est une nouvelle approche de clustering de réseaux, qui exploite à la fois la structure et les attributs des noeuds. Bien que les travaux récents se soient déjà intéressés à l’optimisation des algorithmes de recherche des FCL, peu de travaux sont aujourd’hui menés sur la complémentarité qui existe entre les liens conceptuels et l’approche classique de clustering qui consiste en l’extraction de communautés. Ainsi dans ce papier, nous nous intéressons à ces deux approches. Notre objectif est d’évaluer les relations potentiellement existantes entre les communautés et les FCL pour comprendre la façon dont les motifs obtenus par chacune des méthodes peuvent correspondre ou s’intersecter ainsi que la connaissance utile résultant de la prise en compte de ces deux types de connaissance. Nous proposons pour cela un ensemble de mesures originales, basées sur la notion d’homogénéité, visant à évaluer le niveau d’intersection des FCL et des communautés lorsqu’ils sont extraits d’un même jeu de données. Notre approche est appliquée à deux réseaux et démontre l’importance de considérer simultanément plusieurs types de connaissance et leur intersection.
Extraction et gestion des connaissances
Rennes, France

hal-01312560
https://hal.archives-ouvertes.fr/hal-01312560

Éditeur(s) : HAL CCSD
Résumé : International audience
Human spatial motions determine geographic social contacts that influence the way an information is spread on a population or a community. As mobility is a transverse dimension to social practices it is important to better understand its role. With the Eternal-Return model we propose, we simulate an artificial world populated by heterogeneous agents who differ in their mobility. We have chosen a multi-agent framework perspective for this simulation. We endow the agents with simple rules on how to move around the space and how to establish proximity-contacts. This allows to distinguish different kinds of mobile agents, from sedentary ones to travelers. To summarize the dynamics induced by mobility over time, we define the mobility-based Social Proximity Network as being the network of all distinct contacts between agents. Its properties give insight in the process of information spreading. We conduct simulations to understand how an information can be broadcast when agent-nodes are in motion.
IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Niagara falls, Canada

hal-00874344
https://hal.archives-ouvertes.fr/hal-00874344

Éditeur(s) : HAL CCSD
Résumé : International audience
In this paper, we focus on the very specificity of rumors as pieces of information for modeling their process of propagation. We consider a population of pedestrians walking in a city and we assume that a rumor is transmitted by word of mouth from one to another. Although the diffusion of a rumor is of course a multi-dimensional process driven by sociological, economical and psychological elements, in this first step, we emphasize one main dimension of this complex phenomenon only. This dimension is the neighborhood of individuals likely to spread the information. With a confrontation of two antagonistic properties of the neighborhood that are profusion and scarcity of spreaders, we highlight specific characteristics of rumors. This study could lead to the psychological mechanism involved in the decision for a person to become or not a spreader himself/herself. In summary, we study if scarcity could be the silver bullet explaining how a rumor spreads.
ASONAM 2015 : IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Paris, France

hal-01252936
https://hal.archives-ouvertes.fr/hal-01252936
DOI : 10.1145/2808797.2809299

Éditeur(s) : HAL CCSD Elsevier
Résumé : International audience
These last years, many analysis methods have been proposed to extract knowledge from social networks. As for the traditional data mining domain, these network-based approaches can be classified according to two main families. The approaches based on predictive modelling, which encompass the techniques that analyse current and historical facts to make predictive assumptions about future or unknown events. The approaches based on descriptive modelling, which cover the set of techniques that aim to summarize the data by identifying some relevant features in order to describe how things organize and actually work. In this paper, we review the main descriptive modelling methods of social networks and show for each of them the resulting useful knowledge on a running example. We particularly emphasize on the most recent methods that combine information available on both the network structure and the node attributes in order to provide original description models taking into account the context.
EISSN: 1877-0509

hal-01312383
https://hal.univ-antilles.fr/hal-01312383
DOI : 10.1016/j.procs.2015.05.505

Éditeur(s) : HAL CCSD
Résumé : International audience
More and more animal species are endangered every day on earth. In order to study their adaptation to world and climate change and their chances of survival, numerous initiatives have been taken that mostly need human intrusion into animal communities. Today mobile devices enable researchers to go beyond this limit. In this paper, we propose an original solution that consists on a new framework for detecting individual songs in a bird population and identifying remotely by this way their collective behavior in movements without human interaction. Movement patterns are elicited by analyzing data collected via wireless sensors fitted with microphone. Whereas similar methods use mobile devices fitted on some specimens, we rather propose fixed sensors. We demonstrate that this solution provides a good answer to technical constraints assessed by the context and we discuss results of experimental simulations that allow to define optimized parameters for the architecture to be set up on the ground. Experimental results are provided and show the relative impact of different parameters such as the number of sensors or the population size on the detection rate.
Proceedings of the 8th International Conference on Advances in Mobile Computing and Multimedia (MOMM 2010)

hal-00601641
https://hal.archives-ouvertes.fr/hal-00601641