AlgoTel 2014

16èmes Rencontres Francophones pour les Aspects Algorithmiques des Télécommunications

Link: http://icube-algotel2014.unistra.fr

When Jun 3, 2014 – Jun 6, 2014
Where Le-Bois-Plage-en-Ré, France
Submission Deadline Feb 7, 2014
Notification Due Apr 9, 2014
Final Version Due Apr 25, 2014
Categories algorithm network telecom

Seminar : Statistical physics of opinion and social conflict by Dr. Gerardo Iñiguez from Aalto University (Finland)

Date : December, 3th 2013 at 15:00

Room : IXXI

Since the 19th century many scholars have pondered on the existence of quantitative laws that describe the collective behaviour of a large number of people. Although fields like statistics and sociometry have long studied the dynamical regularities and structural properties of social systems, it is only recently that the description of society has been approached within the framework of statistical physics. The fast development of such ‘physics of social atoms’ is mainly due to the current availability of large datasets on online human behaviour, and includes a myriad of simplified mathematical models aimed at quantifying and predicting social interactions.

In this talk I will present a couple of models for the dynamics of opinion and social conflict, both motivated and qualitatively validated by empirical data. The first model explores the coupled evolution of social network structure and individual opinions regarding a controversial topic, and emulates the segregation of opposing opinion groups seen in a controlled experiment where students discuss drug-legalisation issues. The second model describes the production of a common product by collaborating individuals with diverse opinions, characterising different regimes of conflict that match editorial activity in Wikipedia articles. These results offer an inkling on the promising field of data-driven social dynamics, otherwise known as computational social science.

Séminar: Social aspects of (location) privacy by Kévin Huguenin

Date: Friday, December, 13th, 2013 at 11:00AM
Room: IXXI

Summary:

In this talk, I will present two contributions and an ongoing
project on the social aspects of privacy. More specifically, (1) I will
present the results of a comparative study on the inference of social
ties in pervasive networks (from the stand points of two different
adversaries) and (2) I will present a case where users’ actions
compromise the location privacy of other users.

The first contribution is motivated by the wide deployment of WiFi base
stations in both public spaces and private companies, which poses a
significant threat to the privacy of connected users. Although prior
studies have provided evidence that it is possible to infer the social
ties of users from their location and co-location traces, they lack one
important component: the comparison of the inference accuracy between an
internal attacker (e.g., a curious application running on a mobile
device) and a realistic external eavesdropper in the same field trial.
In this work, we experimentally show that such an eavesdropper is able
to infer the type of social relationships between mobile users better
than an internal attacker. Moreover, our results indicate that by
exploiting the underlying social community structure of mobile users,
the accuracy of the inference attacks doubles.
The second contribution is a study of a concrete and widespread example
of a situation in which users compromise each other’s privacy,
specifically the location-privacy threat created by access points (e.g.,
public hotspots) using Network Address Translation (NAT). Indeed,
because users connected to the same hotspot share a unique public IP
address, a single user making a location-based request is enough to
enable a service provider to map the IP address of the hotspot to its
geographic coordinates, thus compromising the location privacy of all
the other connected users. When successful, the service provider can
locate users within a few hundreds of meters, thus improving over
existing IP-location databases.

Bio:
Kévin Huguenin is a Post-Doctoral Researcher at Ecole Polytechnique
Fédérale de Lausanne (EPFL), Switzerland, in the Laboratory for
Communications and Applications. He received his B.Sc. in computer
science from Ecole Normale Supérieure (ENS) de Cachan — Antenne de
Bretagne and the Université de Rennes I in 2005 and his M.Sc. from the
Université de Nice — Sophia Antipolis in 2007. He obtained a Ph.D. from
the Université of Rennes I in 2010 for his research on misbehavior
detection in large-scale distributed systems (mainly P2P) conducted in
the ASAP Team at IRISA/INRIA Rennes, under the supervision of Anne-Marie
Kemarrec. He has been working at the Vrije Universiteit Amsterdam and
Telefonica Research Barcelona as an intern in 2008 and 2009
respectively, and at McGill University as a post-doctoral researcher in
2011. His research interests include security and privacy in distributed
systems and (mobile) networks.

Project “Real time sociolinguistic: a study on the linguistic variability in Twitter” granted by IXXI and ASLAN

This project is supported by IXXI and ASLAN ( Advanced Studies on LANguage complexity).

ANR-Contenus numériques et interactions: CODDDE project accepted

The CODDDE  (Evolving communities, diffusion and events detection ) project will be granted by ANR.

Http: http://jlguillaume.free.fr/coddde/

Partners are :

  • Laboratoire d’Informatique de Paris 6, Paris, France
  • Laboratoire de l’Informatique du Parallélisme, Lyon, France
  • SME, RTGI SAS Linkfluence, Paris, France

Summary
Complex networks appear in many contexts: sociology, with friendship networks, collaboration networks, computer networks, with the Internet, the World Wide Web, blog networks, P2P networks, biology, with food webs, genetic regulatory networks, metabolic networks, epidemiology, energy and transportation with road networks, power grids, railways, airline routes, but also in economics, linguistics and many others.
The emerging and promising domain of complex networks study, and in particular social ones, has proposed a stream of studies aimed at identifying properties of complex networks, their causes and consequences, describing their evolution and capturing everything into relevant models. These properties are used as key parameters in the study of various phenomena of interest like robustness, spreading of information, ideas or viruses. These questions are very transversal since diffusion phenomena can occur on many different social networks (including online ones) in the form of diseases, ideas, innovations, news or rumor, on computer networks with computer viruses which can propagate directly or by mail, p2p exchange systems, etc…
Whether we consider the evolution of complex networks or the spreading of anything on them, we cannot expect it to be monotonic but rather to be composed of a background evolution with some specific events of prime interest. These events may correspond to abnormal activity on the network: for instance phone-call networks are over saturated for New Year’s Eve, news blogs can be very reactive to specific events and in some occasion one per thousand to one percent of all blogs can talk about a given subject during one day (see today’s highlights on blogpulse.com for instance). Events can also be related to sudden changes in the network structure, e.g. routing tables updates after a link failure. Being able to detect or predict such events is of key interest.
The CODDDE project aims at studying critical research issues in the field of real-world complex networks study:

  • How do these networks evolve over time?
  • How does information spread on these networks?
  • How can we detect and predict unexpected changes in their structure?

In order to answer these questions, an essential feature of complex networks will be exploited: the existence of a community structure among nodes of these networks. Complex networks are indeed composed of internally densely connected groups that have few interactions with one another.
The CODDE project will therefore propose new community detection algorithms to reflect complex networks evolution, in particular with regards to diffusion phenomena and anomaly detection. These algorithms and methodology will be applied and validated on a real-world online social network consisting of more than 10 000 blogs and French media collected since 2009 on a daily basis (the dataset comprises all published articles and the links between these articles) correlated with a twitter dataset.
The consortium of the project comprises two academic partners from large research labs, with a strong experience in complex networks: the Complex Networks team from LIP6-UPMC (Laboratoire d’Informatique de Paris 6 of Université Pierre et Marie Curie), and the LIP-ENS Lyon team (Laboratoire d’Informatique du Parallélisme of the École Normale Supérieure de Lyon), and one industrial partner: the Linkfluence SME, who will be in charge of data collection. Moreover, the expertise of Linkfluence blogs analysts will be used during the results validation phase.

STIC-AmSud: UCOOL project accepted

The UCOOL (Understanding and predicting human demanded COntent and mObiLity) project will be granted by STIC-AmSUD.

Partners are :

  • Laboratório Nacional de Computacão Científica (LNCC) in Brazil
  • Facultad de Ingeniería, Universidad de Buenos Aires (FI/UBA)  in Argentina
  • Universidad Tecnica Federico Santa Maria (USM) in Chile
  • Telecom Sud Paris, in France
  • INRIA Saclay – Ile de France and INRIA Grenoble – Rhône-Alpes in France

Project goals

  • To define solutions for the identification and modelling of correlations between the user mobility – describing changes in the user positioning and the current environment he/she is in – and the traffic demand he/she generates.
  • To develop techniques to predict the future user mobility and demanded content at both microscopic (i.e., individual user) and macroscopic (i.e., user communities) levels. The insights obtained from these studies will drive the proposal of new content- and context-aware networking protocols and/or applications that will explicitly leverage the mobility and interest of users to improve the perceived quality of their services and consequently, improve user satisfaction.
  • To increase and to strength the scientific exchange, synergy and expertise between France and Latin-American countries for future research.

Abstract
Finding new ways to manage the increased data usage and to improve the level of service required by the new wave of applications for smartphones is an essential issue nowadays. The improved understanding of user mobility (i.e. the context they experience) and the content they demand is of fundamental importance when looking for solutions for this problem in the modern communication landscape. The resulting knowledge can help at the design of more adaptable networking protocols or services as well as can help determining, for instance, where to deploy networking infrastructure, how to reduce traffic congestion, or how to fill the gap between the capacity granted by the infrastructure technology and the traffic load generated by mobile users.

Building on previous research efforts in the fields of social wireless networking,
opportunistic communications, and content networking, the UCOOL project will derive appropriate models for the correlation between user interests and their mobility. Lots of studies have characterized the mobility of mobile nodes based on real world data traces, but knowledge about the interactions with user interests in this context is still missing. Moreover, efficient prediction algorithms will be derived to forecast the node’s mobility as well as interests. Predictions of future events (i.e., related to human mobility or the demanded content) can allow networking protocols or services to anticipate and react upon actions based on patterns learnt from human daily life. The partners involved in the project, located in France, Brazil, Chile, and Argentina, present complementary background and skills to address the context-content correlation and related prediction in mobile wireless networks. The main expected outcome of this project is the establishment and consolidation of a thematic and collaborative research network between the partners to deal with the challenges of leveraging human content and context knowledge. In doing so, we expect new tools and analyses to be developed as well as a new scientific foundation to be established to deal with increasing data usage in wireless mobile networks.

Workshop on Dynamic Networks 2013

November 5-7th, 2013 — Buenos Aires, Argentina

The program is available at: http://cnet.fi.uba.ar/wdn13/

:: Call for Papers ::

Large-scale networks with complex interaction patterns between its elements are abundantly found in a wide variety of fields such as Communications, social networks, blog & collaborative networks, WWW, P2P, DTN, Sensors, etc..

In the last decade many research efforts contributed to establish the basic ideas of a network science in order to characterize, model, and analyze such complex networks. This new science has found applications in a wide variety of fields in theoretical physicists, epidemiology, biology, economics, and computer science.

The networking layer in communication systems as well as the application layer, such as the social network, are relevant distinct research fields in the study of complex interacting networks. The present workshop is devoted to understand how is the mutual interaction between these layers, and the potential interplay, between dynamics taking place in each of them.

This workshop aims at exchanging and discussing ideas about the complex systems field, mainly oriented (but not limited to) the following topics:
Bio-inspired networks
Correlation of different types of social networks
Delay tolerant and disruption tolerant networks (DTN)
Discrete optimization and algorithms
Dynamical processes (proliferation, diffusion etc.)
Game theoretic models, pricing and incentives on dynamic networks
Implications of social networking on distributed network/systems
Mobility modeling and management
Robustness & Stability of dynamical networks
Sensor Networks
Social networks
Characterizing and analyzing dynamic networks
Tools and methods for collecting, analyzing, or visualizing data from dynamic networks