Annonce en lien avec l’Action/le Réseau : aucun

Laboratoire/Entreprise : Laboratoire ETIS
Durée : 5
Contact : claudia.marinica@ensea.fr
Date limite de publication : 2010-04-01

Contexte :
Internship 6 months at the ETIS Lab UMR 8051 (Paris area)
(University Paris Seine, University of Cergy Pontoise, ENSEA, CNRS)

Title
Evolution of personal online social networks: new techniques and comparisons

Supervisors
Claudia Marinica, ETIS lab, MIDI team
https://perso-etis.ensea.fr//marinica/

Sujet :
Description

Nowadays, Online Social Networks (OSNs) allow to the users to be in direct contact, to exchange information, messages, etc.; these networks evolve with the evolution of the life of the users. Moreover, a social network can be seen as a group of several smaller networks centered around one individual. These small networks are called Online Personal Networks (ONPs) [1], because they are composed of a central individual (the ego) and several additional individuals (called the alters) that are connected to the ego directly or indirectly.
Moreover, the OPNs (as classical OSNs) evolve over time, but, given that their study is very recent, it is still not clear if their evolution is comparable to the one of OSNs. In the last studies that we conducted in [2], we analyzed the evolution of collaboration networks (such as DBLP – https://dblp.uni-trier.de/xml/) by analyzing the evolution of the values of several metrics. Currently, we are proposing a specific evolution model.
This internship deals with two issues: (1) the study of the OPNs by using other techniques in order to compare and validate the results obtained in our previous studies, and (2) make available to the community the tools developed so far.
The first issue concerns the use of data mining techniques in order to asses the evolution of OPNs. Indeed, a specific representation of a social network at a time t could allow to extract a set of information like “if a network has 2 new nodes at time t, then it also has the tendency to has 2 new nodes in time t+1”. In this context, several challenges can be outlines, such as the choice of the data mining technique and the modelling of the data in order to apply the chosen technique. These challenges are clearly related, and several techniques can be used, depending on the expected result.
The second issue concerns the development of a tool for the analyses of the evolution of OPNs selected from an OSN. This tool would be used by the experts studying a specific OSN and who would like to understand how the OPNs from the OSN evolve over the time. In this part, existing developments (produced during an ongoing PhD) can/should be used and should be completed by the propositions made during the internship.

Objectives

In the last years, a set of studies on the analysis of social networks was dedicated to the understanding of their evolution over time. These works tried to develop generative models for big networks reproducing the properties of real networks, like the scale free networks (where the degrees follow a power law), the high clustering coefficient, and low shortest path (known as small-world phenomenon [3]).
Nevertheless, the studies related to the evolution of personal networks and quite limited. For example, some of the works tried to understand if the personal online networks are comparable to the offline ones studied in sociology [5]; these works focused on the evolution of each level of alters in a personal network by using the conclusions from the studies on cognitive charge of an individual [4].
Even if these conclusions are important and that they can be used in order to modify the evolution of these types of relations, they do not allow to understand the evolution of the structure of a personal network. In [2], we propose a methodology to study the evolution of personal networks based on the evolution of a set of metrics.
This internship had two objectives:
1/ The first one is to propose a new methodology for the analysis of the evolution of OPNs which will allow us to compare the results already obtained in our previous studies. The new methodology will integrate data mining techniques. We propose here to use the frequent pattern mining technique because it allows us to detect frequent pattern of the evolution of OPNs. In this context, a state of the art should be done, but also a study in order to chose a specific pattern among the different existing patterns: transactional, sequential, graph, etc.
2/ The second one concerns the development of an online tool accessible by the community and the experts. Mainly the tool should:
• Display the OSN;
• Select the ego and other parameters allowing to extract one or several OPNs;
• Display the OPNs;
• Select the metric to compute on the chosen OPNs;
• Display the value of the metrics of OPNs;
• Display the result of the data mining technique;
• Compare the two previous results.

References

[1] Sarah Djemili, Claudia Marinica, Maria Malek, Dimitris Kotzinos (2016). A Definitions’ Framework for Personal/Egocentric Online Social Networks. 7ème conférence sur les modèles et l’analyse des réseaux: Approches mathématiques et informatiques (MARAMI’16).
[2] Sarah Djemili, Claudia Marinica, Maria Malek, Dimitris Kotzinos. Personal Networks of Scientific Collaborators: A Large Scale Experimental Analysis of Their Evolution. Information Search, Integration, and Personlization. Communications in Computer and Information Science., 760, Springer, Cham, 2017.
[3] Travers, Jeffrey and Milgram, Stanley. The small world problem. Phychology Today. pages: 61-67. 1967.
[4] Arnaboldi, Valerio and Conti, Marco and Passarella, Andrea and Dunbar, Robin. Dynamics of personal social relationships in online social networks: a study on twitter. Proceedings of the first ACM conference on Online social networks. pages: 15-26, 2013.
[5] Sutcliffe, Alistair and Dunbar, Robin and Binder, Jens and Arrow, Holly. Relationships and the social brain: integrating psychological and evolutionary perspectives. British journal of psychology. pages: 149-168. 2012.

Profil du candidat :
Candidate
We are looking for a 2nd year Master Student (M2) with knowledge in social networks and/or data mining techniques.

Application info
The position will be open until filled. Starting date before 01/04/2019. Apply by sending your CV, recommendation/motivation letters and grades for at least the 1st and 2nd year of the Master (M1 and M2) to:
Claudia.Marinica@u-cergy.fr

Formation et compétences requises :
2nd year Master Student (M2) with knowledge in social networks and/or data mining techniques

Adresse d’emploi :
2 avenue Adolphe Chauvin, 95300, Cergy-Pontoise

Document attaché : sujet_stage_eng.pdf

Annonce en lien avec l’Action/le Réseau : aucun

Laboratoire/Entreprise : laboratoire d’informatique
Durée : 6 semaines
Contact : colin.riba@ens-lyon.fr
Date limite de publication : 2016-02-07

Contexte :
À l’issue de leur première année de cours (L3), les élèves en
informatique de l’École Normale Supérieure de Lyon doivent
effectuer un stage de six semaines (quelque part entre
juin et fin août) dans une équipe de recherche universitaire ou
associée. Nous vous sollicitons pour proposer un sujet de stage et/ou
diffuser cette demande autour de vous.

Sujet :
Les informations concernant ces stages sont disponibles en ligne :
http://www.ens-lyon.fr/DI/stageL3/

La soumission de propositions de stages s’effectue également en ligne :
http://www.ens-lyon.fr/DI/stageL3/submit.php

– Ouverture : 15 décembre 2015
– Fermeture : 7 février 2016

Profil du candidat :
N’hésitez pas à faire suivre cette information à des collègues qui
peuvent être intéressés.

En vous remerciant de l’aide que vous pouvez apporter à l’organisation de ces stages,

Bien cordialement,

Colin Riba

Formation et compétences requises :
NA

Adresse d’emploi :
NA

Document attaché :

Annonce en lien avec l’Action/le Réseau : aucun

Laboratoire/Entreprise : CEA LIST (DM2I/LADIS)
Durée : 6 mois
Contact : lorene.allano@cea.fr
Date limite de publication : 2016-03-01

Contexte :
Le CEA LIST (http://www-list.cea.fr/) est un centre d’innovation et de recherche technologique atour aux nouvelles technologies de l’information et de la communication. Le CEA LIST est basé sur le plateau de Saclay et appartient à l’université Paris Saclay. Au sein du CEA LIST notre laboratoire est dédié aux Data Analytics et au Big Data.

Sujet :
Bases de données distribuées sur architecture Big Data.

Pour ce stage de Master 2, nous recherchons un candidat ingénieur R&D autonome et motivé par les nouvelles technologies et les architectures Big Data pour développer un environnement de base de données distribuées dans le cadre d’un projet de recherche translationelle. L’objectif de ce stage sera dans un premier de faire un benchmark des systèmes de gestion de bases de données distribuées et NoSQL et ensuite de mettre en œuvre le système adapté au cahier des charges du projet sur notre architecture Big Data. Le développement sera de préférence réalisé en Python.

Profil du candidat :
étudiant en master 2 ou dernière année d’école d’ingénieur profil Big Data.

Formation et compétences requises :
Python, Spark, Hadoop, NoSQL

Adresse d’emploi :
centre de Saclay – Digiteo Labs
DM2I / LADIS – PC 192
91191 Gif sur Yvette Cedex
http://www-list.cea.fr/

Document attaché :