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
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 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
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 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
In the network modeling area, the most widely used definition of a "pattern" is that of a subgraph, a notion that considers only the network topological structure. While this definition has been very useful for extracting subgraphs frequently found in a network or a set of networks, it does not take into account the node attributes, an intrinsic component of social networks that often provides relevant information on the role or the position of a node in a network. In this paper, we propose a novel approach for extracting frequent patterns in social networks, called frequent link mining, based on the search for particular patterns that combine information on both network structure and node attributes. This kind of patterns, that we call frequent links, provides knowledge on the groups of nodes connected in the social network. In this article, we detail the method proposed for extracting frequent links and discuss its flexibility and its complexity. We demonstrate the efficiency of our solution by carrying out qualitative and quantitative studies.
Advances in Database (Weston, Conn.)s and Information Systems
Poznań, Poland

hal-00767050
https://hal.archives-ouvertes.fr/hal-00767050
DOI : 10.1007/978-3-642-33074-2_28

Éditeur(s) : HAL CCSD
Résumé : International audience
The increasing development of world trade has promoted the spread of diseases. Recent events, like the H1N1 outbreak and spread, have confirmed this tendency. Everyone could observe that this kind of infectious disease often has the ability to cross countries borders and spread rapidly. Preventing, monitoring and controlling the outbreak of these diseases have appeared to be a public health problem of primary importance for years. However, although numerous works have been conducted in order to understand and study epidemics, this subject remains a topical issue because of the complexity of the phenomena. In this paper, we present new opportunities offered by the field of social networks for understanding the spread of infectious diseases. This work focuses both on techniques already used in epidemiology, and very recent works in social network analysis that open new perspectives. We give an overview of methods currently used to model and study the spread of diseases and future directions that the social network approach allows to glimpse.
2011 Fifth International Conference on Research Challenges in Information Science (RCIS)
Gosier, Guadeloupe

hal-00624364
https://hal.archives-ouvertes.fr/hal-00624364
DOI : 10.1109/RCIS.2011.6006866

Éditeur(s) : HAL CCSD
Résumé : International audience
The extraction of knowledge from social networks is an area that has experienced significant growth in recent years. Indeed, thanks to the improvement of storage and calculation capacities, and the heterogeneity of data that can currently be extracted, much effort has been made to go beyond traditional knowledge, by proposing new kinds of patterns that take into account the context. However, while many works were interested in designing new patterns of knowledge or in optimizing existing approaches, few studies have been focused in merging patterns and on the useful knowledge emerging from such fusions. In this work, we focus on two network clustering approaches, able to extract two distinct kinds of patterns, and we seek to understand both the intersections that can exist between them and the knowledge that emerges from their fusion. The first is the classical nodes clustering approach that consists in searching for communities into a network. The second is the search for frequent conceptual links, a new link clustering approach that aims identifying frequent links between groups of nodes sharing common attributes. We propose a set of original measures that aim to evaluate the amount of shared information between these patterns when they are extracted from a same network. These measures are applied to three datasets and demonstrate the interest in simultaneously considering several sources of knowledge.
Advances in Knowledge Discovery and Management (AKDM)

hal-01514227
https://hal.archives-ouvertes.fr/hal-01514227
DOI : 10.1007/978-3-319-45763-5_13

Éditeur(s) : HAL CCSD
Résumé : International audience
This paper proposes a new knowledge discovery method called FLMin to discover frequent patterns in a social network. The algorithm works without previous knowledge on the network and exploits both the structure and the attributes of nodes to extract regularities called Frequent Links. Unlike traditional works in this area that solely exploit structural regularities of the network, the originality of FLMin is its ability to gather these two kinds of information in the search for patterns. In this paper, we detail the method proposed for extracting frequent links and discuss its complexity and its flexibility. The efficiency of our solution is evaluated by conducting qualitative and quantitative studies for understanding how behaves FLMin according to different parameters.
Networked Digital Technologies
Dubai, United Arab Emirates

hal-00768431
https://hal.archives-ouvertes.fr/hal-00768431
DOI : 10.1007/978-3-642-30567-2_38

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
Numerous social network mining methods have been proposed until now for addressing social mining tasks and specially searching for communities or frequent social patterns. However, the degree of complementarity of these methods has been very little studied. In this paper, we focus on two knowledge extraction processes in social networks: a link-based clustering that may extract social communities and a recent approach that searches for frequent conceptual link involving both clustering and search for frequent social patterns. We explore how the models extracted by each method may match and which potential useful knowledge they may provide. Our objective is to evaluate the potential relationships between communities and frequent conceptual links. For this purpose, we propose a set of measures for evaluating the degree to which these patterns extracted from the same dataset are matching. Our approach is applied on two datasets and demonstrates the importance of considering simultaneously various kinds of knowledge and their complementarity.
IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Niagara falls, Canada

hal-00874351
https://hal.archives-ouvertes.fr/hal-00874351

Éditeur(s) : HAL CCSD
Résumé : International audience
In this work, we propose a novel approach for the discovery of frequent patterns in a social network on the basis of both vertex attributes and link frequency. With an analogy to the traditional task of mining frequent item sets, we show that the issue addressed can be formulated in terms of a conceptual analysis that elicits conceptual links. A social-based conceptual link is a synthetic representation of a set of links between groups of vertexes that share similar internal properties. We propose a first algorithm that optimizes the search into the concept lattice of conceptual links and extracts maximal frequent conceptual links. We study the performances of our solution and give experimental results obtained on a sample example. Finally we show that the set of conceptual links extracted provides a conceptual view of the social network.
IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)
Istanbul, Turkey

hal-00786161
https://hal.archives-ouvertes.fr/hal-00786161
DOI : 10.1109/ASONAM.2012.15

Résumé : 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
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." ln 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.

http://www.theses.fr/2012AGUY0569/document

Éditeur(s) : HAL CCSD Springer
Résumé : International audience
he search for frequent conceptual links (FCL) is a new network clustering approach that exploits both the network structure and the node properties. While recent works have been focused on the optimisation of the FCL extraction, no study has been conducted on the matching or the intersections between the conceptual links and the classical clustering approach which consists in extracting communities. In this paper, we focus on these two approaches. Our objective is to evaluate the possible relationships existing between FCL and communities to understand how the patterns obtained by each kind of methods may match as well as the useful knowledge resulting from the intersections of these two kinds of knowledge. For this purpose, we propose a set of original measures, based on the notion of homogeneity, assessing the level of shared information between these patterns when they are extracted from the same dataset. The measures are applied on three datasets and demonstrate the importance of considering simultaneously various kinds of knowledge and their intersections.
ISSN: 1869-5450

hal-01312313
https://hal.archives-ouvertes.fr/hal-01312313
DOI : 10.1007/s13278-014-0197-1

Éditeur(s) : HAL CCSD
Résumé : International audience
Dans ce travail, nous analysons les données concernant les articles publiés à la conférence EGC. Notre objectif est d’identifier et de comprendre les tendances en matière de collaborations. Pour ce faire, nous adoptons une modélisation descriptive, à travers une approche réseau qui consiste à générer tout d’abord le réseau de collaborations des auteurs à partir des données. Nous enrichissons ensuite les noeuds de ce réseau d’une dizaine d’attributs individuels extraits à partir des données. Enfin, nous recherchons des vues conceptuelles, une approche récente de clustering de liens, qui permet de synthétiser des réseaux en mettant en évidence les ensembles d’attributs retrouvés fréquemment liés dans le réseau. Les résultats obtenus montrent les tendances existantes dans les comportements de collaborations. Dans ce papier, nous présentons ces tendances et montrons comment elles évoluent selon différents seuils d’extraction.
Extraction et gestion des connaissances
Reims, France

hal-01312559
https://hal.archives-ouvertes.fr/hal-01312559