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 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 paper proposes a new knowledge discovery method called MAX-FLMin for extracting frequent patterns in social networks. Unlike traditional approaches that mainly focus on the network topological structure, the originality of our solution is its ability to exploit information both on the network structure and the attributes of nodes in order to elicit specific regularities that we call "Frequent Links". This kind of patterns provides relevant knowledge about the groups of nodes most connected within the network. First, we detail the method proposed to extract maximal frequent links from social networks. Second, we show how the extracted patterns are used to generate aggregated networks that represent the initial social network with more semantics. Qualitative and quantitative studies are conducted to evaluate the performances of our algorithm in various configurations.
Database (Weston, Conn.) and Expert Systems Applications
Vienna, Austria

hal-00767052
https://hal.archives-ouvertes.fr/hal-00767052
DOI : 10.1007/978-3-642-32600-4_35

Éditeur(s) : HAL CCSD Royal Society, The
Résumé : International audience
Individuals can play different roles in maintaining connectivity and socialcohesion in animal populations and thereby influence population robustnessto perturbations.We performed a social network analysis in a reef shark populationto assess the vulnerability of the global network to node removal underdifferent scenarios. We found that the network was generally robust to theremoval of nodes with high centrality. The network appeared also highlyrobust to experimental fishing. Individual shark catchability decreased as afunction of experience, as revealed by comparing capture frequency and sitepresence. Altogether, these features suggest that individuals learnt to avoidcapture, which ultimately increased network robustness to experimentalcatch-and-release. Our results also suggest that some caution must be takenwhen using capture–recapture models often used to assess population sizeas assumptions (such as equal probabilities of capture and recapture) maybe violated by individual learning to escape recapture.
EISSN: 1471-2954

hal-01561754
https://hal-univ-perp.archives-ouvertes.fr/hal-01561754
DOI : 10.1098/rsbl.2016.0824

Éditeur(s) : HAL CCSD Springer
Résumé : International audience
The study of information dissemination in social networks is of particular importance in many areas as marketing, politics and security for example. Various strategies are being developed to disseminate information, those aimed at disseminating information widely and those aimed at disseminating information in a more confidential manner to make it scarce. In this paper, we adapt a model dedicated to spreading rumours by word of mouth in a physical space to the context of social networks. We compare two modes of dissemination based on profusion or scarcity and study the impact of the choice of the initial node. The results obtained show to what extent each mode exploits the social network topology and especially the influence of hubs.
International Conference on Complex Networks and Their Applications (COMPLEX NETWORK)
Lyon, France

hal-01637573
https://hal.archives-ouvertes.fr/hal-01637573