• Aucun résultat trouvé

Preface

N/A
N/A
Protected

Academic year: 2022

Partager "Preface"

Copied!
3
0
0

Texte intégral

(1)

Preface

Social network analysis (SNA) is a multidisciplinary research area that has at- tracted many researchers from different disciplines such as Physics, Mathemat- ics, Sociology, Biology and Computer Science, and has been studied according to different approaches and techniques. A social network is a dynamic structure (generally represented as a graph) of a set of entities/actors (nodes) together with links (edges) between them. The explosive growth of online social media has provided users with the opportunity to create and share digital content on a range hardly imaginable a few years ago. Indeed, massive participation has transformed online social networks into cores of social activity and a critical information vehicle. This is reflected by the number of news, opinions, and reviews that are constantly posted and discussed on these networks. The size and diversity of user-generated content create an opportunity for identifying central and influential players, behavioral trends and user communities. Formal concept analysis (FCA) is a branch of lattice theory motivated by the need for a clear formalization of the notions of concept and conceptual hierarchy. It has been successfully used for conceptual clustering and association rule mining.

We believe that formal concept analysis and its extensions can contribute to the analysis and mining of social networks, e.g., affiliation and interaction net- works, and possibly more complex structures. The first studies on using FCA for key player and community detection were conducted in the nineties. As the previous SNAFCA event (see http://snafca.free.fr), the second edition was co- located with the International Conference on Formal Concept Analysis (ICFCA 2015) and was held in Nerja (Spain) on June 25, 2015. The objective of the SNAFCA’2015 workshop was to bring together researchers and practitioners to discuss the ways the two research areas can benefit from each other’s advances and study the potential of formal concept analysis in proposing new and efficient solutions to key topics in SNA such as central/influential actor identification, community detection and evolution, link prediction, and network reorganiza- tion. The workshop program included an introductory talk titled “Using FCA for Social Network Analysis” by Rokia Missaoui, regular talks, followed by a panel discussion “Why FCA for SNA?” moderated by Sergei Kuznetsov. The proceedings of SNA workshop include seven papers that were reviewed by at least two reviewers.

During the panel discussion the following issues were raised and discussed:

Finding the meeting point of FCA, Descriptions logic and SNA Having clearer definitions of SNA goals in FCA terms Alignment of the notions of central/peri- pheral nodes by Freeman with SNA centrality User-controlled filters Privacy issues Representing the dynamicity in social networks by FCA means, includ- ing the key topic of community detection and evolution R package for FCA Proximity and similarity measures through FCA means.

To conclude this preface, we would like to thank all the authors for their contributions and the organizers of ICFCA’2015 for their kind support in hosting SNAFCA’2015. Our warm thanks go also to the reviewers for their careful review of the submissions and their useful comments and suggestions. Finally,

(2)

the success of this event was possible thanks to the active participation of thirty- five attendees and the dedication of Sid Ali Selmane, who acted as a webmaster of the workshop site.

November 2015 Sergei O. Kuznetsov,

Rokia Missaoui, Sergei Obiedkov

(3)

Organizing Committee

Rokia Missaoui, Universite du Quebec en Outaouais, Canada

Sergei O. Kuznetsov, National Research University Higher Schools of Economics, Moscow, Russia

Sergei Obiedkov, National Research University Higher Schools of Economics, Moscow, Russia

Program Committee

Peter Eklund, IT University in Copenhagen Reda Alhajj, University of Calgary, Canada Karell Bertet, Universite de La Rochelle, France

Mohamed Bouguessa, Universite du Quebec a Montreal, Canada Leonard Kwuida, Bern University of Applied Sciences, Switzerland

Sergei O. Kuznetsov, National Research University Higher School of Economics, Russia

Jurgen Lerner, University of Konstanz, Germany

Rokia Missaoui, Universite du Quebec en Outaouais (UQO), Canada Amedeo Napoli, Universite de Lorraine, France

Lhouari Nourine, Universite Blaise Pascal, France

Sergei Obiedkov, National Research University Higher School of Economics, Russia

Camille Roth, CNRS/EHESS, Paris, France

Sebastian Rudolph, University of Karlsruhe, Germany Gerd Stumme, University of Kassel, Germany

Petko Valtchev,Universite du Quebec a Montreal (UQAM), Canada

Références

Documents relatifs

(a) Interstitial porosity estimated from discrete samples versus excess lithostatic stress for slopes sediments at Sites C0001 (red), C0004 (green), C0008 (blue), and C0006 (purple)

Other accounts suggest that ants’ discrimination abilities allow exquisite odor representation of CHCs, both of colony members and alien individuals (21, 22); ants would thus learn

PPP: DAL Co., Khartoum State and the National Social Insurance Fund PPP: Grand Real estate company and Khartoum State Private investments PPP: Public real estate

We used a mixed multinomial logistic regression model to test the following factors for association with pain intensity (mild versus no pain, and moderate to severe versus no pain),

Unvaccinated girls age 16–18 years in tanzania, who reported ever having had sex, were consented, enrolled and tested for the presence of HPV Dna in vaginal samples collected

Keywords: Shifted Chebyshev polynomials, Operational matrix, Generalized fractional pantograph equations, Numerical solution, Error analysis.. ∗

Formation and structure of a sterically protected molybdenum hydride complex with a 15-electron configuration: [(1,2,4-C5H2 tBu 3)Mo(PMe3)2H] +.. Miguel Baya, Jennifer

Pour cela nous avons utilisé la technique DVFS pour réduire l’énergie consommer par CPU et minimiser le temps de réponse lors d’exécution d’une requête dans des bases