Lyon, France, MAY 11-13, 2016

Network Science Thematic Semester Series of Interdisciplinary Workshop

http://netspringlyon.fr

*** Scope and Topics:

The first aim of this workshop is to enhance our understanding of
the links between individuals, social structure, and language
usage. These questions should be addressed by the detailed analysis
of recently available large digital datasets, like ones collected
in Twitter and other systems. These datasets include the social
interactions and the utterances of a large number of individuals,
which allows for the coupled analysis of the social network and
language variation and change as a function of time. Our goal is to
contribute to the interdisciplinary fields of computational
sociolinguistics, network science, data-driven and computational
approaches to language and social network. We will bring together
researchers focusing on network linguistics, from different fields:
machine learning, data analytics, data mining, computational modeling,
large-scale graph-structured high-dimensional data, low-dimensional
representations by dimensionality reduction. A second objective of
the workshop will be to discuss about these types of methods, which
follow often from a data driven approach, especially for their
application to social media dataset.

*** Keynote Speakers:

– Stéphanie Barbu (UMR 6552 EthoS)
– Richard Benton (University of Illinois)
– Jacob Eisenstein (Georgia Tech)
– Alfred Hero (University of Michigan)
– José M. F. Moura (Carnegie Mellon University)

*** Submission of abstracts:

We invite you to submit a one or two-page abstract.

Submissions are done via our EasyChair submission link:
https://easychair.org/conferences/?conf=d2netlang

It is required that at least one author of each accepted paper
register and attend the Workshop on Data Driven Approach to Networks
and Language to present their work.

*** Important Dates

– Abstract submission deadline: JANUARY 29th, 2016
– Notification to authors: FEBRUARY 27th, 2016
– Conference date: MAY 11-13, 2016

*** ORGANIZING COMMITTEE

– Jean-Pierre Chevrot (University Grenoble Alpes, LIDILEM)
– Eric Fleury (ENS de Lyon/Inria, LIP)
– Márton Karsai (ENS de Lyon/Inria, LIP)
– Jean-Philippe Magué (ENS de Lyon, ICAR)
– Matthieu Quignard (ENS de Lyon, ICAR)

*** Network Science Thematic Semester

The thematic semester on Network Science is organized by the
DANTE Inria team (Ecole Normale Supérieure de Lyon, Université
Claude Bernard (Lyon 1), CNRS, Laboratoire de l’Informatique
du Parallélisme — UMR5668), the SiSyPhe team (Ecole normale
supérieure de Lyon, Université Claude Bernard (Lyon 1), CNRS,
Laboratoire de Physique — UMR5672), the Centre de Physique
Théorique — UMR-7332, the Excellence Laboratory MILYON —
Mathematics and Fundamental Computer Science in Lyon, the
Institute of Scientific Interchange — (ISI) and the IXXI
(Complex Systems Institute in Rhône-Alpes).

This program intends to cover both the basics of and recent
advances in Network Science and Data Analytics for Networks.
In this program, we aim to bring together world-known experts
from the fields of mathematics, physics, signal processing,
computer science, social science, epidemiology and linguistic
to discuss and enhance our understanding about the interaction
between the structure, evolution, and coupled dynamical
processes of complex networks.

We welcome participants from graduate student level to the
level of experts in the subject. Limited number of travelling
support for students is available (through the organizers).

Lyon, France, JUNE 20-22, 2016

Network Science Thematic Semester Interdisciplinary Workshop Series

http://netspringlyon.fr

*** Scope and Topics

The dynamical evolution of complex networks and ongoing processes
are one of the hottest contemporary directions in Network Science.
The main aim of this workshop is to bring together experts in this
field focusing on the description, observation, and modeling of the
dynamics on and of networks. “Dynamics on networks” refers to the
different types of processes (e.g. proliferation, diffusion, random
walk, etc.) that take place on networks, with functionality/efficiency
strongly affected by the topology as well as the dynamics of
interactions. On the other hand, “Dynamics of networks” mainly
refers to time-varying interactions, which defines temporal networks
on the finest possible level, which are crucial to understand the
evolution and the emerging properties of complex networks. It has
become clear that properties such as stability, robustness, connectivity,
etc. depend on the temporal evolution of networks driven by higher
order structural and temporal correlations.

A motivation of the workshop is also to bring together scientists
interested in the development of methodologies to study complex networks
and data on graphs, especially in the domains of computer science,
graph-theoretical algorithms, graph-signal processing, data analytics,
physics, computational social science, epidemiology, econometrics and
social network theory.

The workshop expects multi-disciplinary research contributions to
study common problems in systems exhibiting a complex network
structure (e.g., biological systems, linguistic systems, social
systems and various other man-made systems like the Internet,
WWW, peer-to-peer systems etc.). Accordingly, one of the major
goals of the workshop is to build bridges between different
research areas and their corresponding communities.

*** Invited speakers:

– Kimmo Kaski (Aalto University, Finland)
– Renaud Lambiotte (University of Namur, Belgium)
– Nicola Perra (Greenwich University, UK)
– Alejandro Ribeiro, TBC (University of Pensilvania, USA)
– Camille Roth, TBC (CNRS, Centre Marc Bloch, Germany)

*** Submission of abstracts:

We invite you to submit a one or two-page abstract.

Submissions are expected via the following EasyChair submission link:
https://easychair.org/conferences/?conf=do2net

It is required that at least one author of each accepted contribution
register and attend the workshop to present the corresponding work.
Registration also assures the participation to the workshop in case
of limitations due to high participation number.

*** Important Dates

– Abstract submission deadline: FEBRUARY 24h, 2016
– Notification to authors: MARCH 24th, 2016
– Conference date: JUNE 20-22, 2016

*** ORGANIZING COMMITTEE

– Pierre Borgnat (CNRS, Laboratoire de Physique) – pierre.borgnat@ens-lyon.fr
– Eric Fleury (ENS de Lyon/Inria, LIP) – eric.fleury@inria.fr
– Paulo Gonçalves (Inria, LIP) – paulo.goncalves@inria.fr
– Márton Karsai (ENS de Lyon/Inria, LIP) – marton.karsai@ens-lyon.fr

*** Network Science Thematic Semester

The thematic semester on Network Science is organized by the
DANTE Inria team (Ecole Normale Supérieure de Lyon, Université
Claude Bernard (Lyon 1), CNRS, Laboratoire de l’Informatique
du Parallélisme — UMR5668), the SiSyPhe team (Ecole normale
supérieure de Lyon, Université Claude Bernard (Lyon 1), CNRS,
Laboratoire de Physique — UMR5672), the Centre de Physique
Théorique — UMR-7332, the Excellence Laboratory MILYON —
Mathematics and Fundamental Computer Science in Lyon, the
Institute of Scientific Interchange — (ISI) and the IXXI
(Complex Systems Institute in Rhône-Alpes).

This program intends to cover both the basics of and recent
advances in Network Science and Data Analytics for Networks.
In this program, we aim to bring together world-known experts
from the fields of mathematics, physics, signal processing,
computer science, social science, epidemiology and linguistic
to discuss and enhance our understanding about the interaction
between the structure, evolution, and coupled dynamical
processes of complex networks.

We welcome participants from graduate student level to the
level of experts in the subject. Limited number of travelling
support for students is available (through the organizers).

Marseille, France, from July 11th to 13th, 2016

www.complexnets2016.org
We invite contributions in all areas of network science, including (but not limited to) theory of complex networks, network data analysis, temporal networks, multiplex networks, online social networks, biological networks, economic networks, transport and infrastructure networks, dynamical processes on complex networks,… Contributions of interdisciplinary nature are particularly welcome.

Contributions, consisting of a 2-pages abstract in pdf format including a figure, should be submitted through Easychair using the following link:
https://easychair.org/conferences/?conf=complexnetworks2016
Contributions should also mention if oral or poster presentation is preferred.

DEADLINE: January 15th, 2016.

Authors will be notified at the end of February 2016.

For more information, see the website http://www.complexnets2016.org

REGISTRATION: http://complexnets2016.org/program-and-registration

The organizers
Alain Barrat (Centre de Physique Théorique, Marseilles, France & ISI Foundation, Turin, Italy)
Ciro Cattuto (ISI Foundation, Turin, Italy)
Eric Fleury (ENS Lyon & IXXI, Lyon, France)
Bruno Gonçalves (NYU Center for Data Science, USA)
Marton Karsai (ENS Lyon & IXXI, Lyon, France)
Xavier Leoncini (Centre de Physique Théorique, Marseilles, France)

11-15 January, ENS Lyon site Monod, Amphi B, 9:30-16:45

Teachers: Ciro Cattuto, Laetitia Gauvin et André Panisson (ISI Torino)
Local contact: Márton Karsai (marton.karsai@ens-lyon.fr)
The main page of the course can be found here.

The course aims to provide basic skills for analysis and statistical modeling of data, with special attention to machine learning both supervised and unsupervised. An important objective of the course is the operational knowledge of the techniques and algorithms treated, and for this aim the lectures will focus on both theoretical and practical aspects of machine learning, and for the practical part it is required to have a good knowledge of programming, preferentially in Python language. The expected outcomes include (1) understanding the theoretical foundations of machine learning and (2) ability to use some Python libraries for machine learning in the context of simple applications.

Topics will include:
– The major paradigms of learning from data, the learning problem, the feasibility of learning
– The architecture of machine learning algorithms: model structure, scoring, and model selection ­ The theory of generalization, model complexity, the approximation­generalization tradeoff, bias and variance, the learning curve
– Score functions and optimization techniques. Gradient descent and stochastic gradient descent.
– Validation and Cross­Validation: validation set, leave­one­out cross validation, K­fold cross­validation
– Linear Models: linear classification, linear regression, ordinary least squares, logistic regression, non­linear transformations
– Non­linear models for classification: support vector machines, tree models, nearest­neighbor methods, Naive Bayes
– Overfitting and Regularization: model complexity and overfitting, commonly used regularizers, Lasso.
– Unsupervised learning: cluster analysis, the K­means algorithm, hierarchical clustering
– Feature selection and dimensionality reduction: Singular Value Decomposition, Matrix Factorisation
– Information retrieval, text representation and classification, term weighting

Overview of the theoretical aspects of machine learning will be followed by the application of algorithms in real problems such as: image classification, text mining, spam detection… The exercises will be implemented with the help of an interactive Python environment, with the use of standard tools for data analysis and visualization, such as the Scientific Python stack, Scikit­Learn, Pandas and NLTK.

Date: 23rd November 2015 between 14:00 and 15:00
Place: ENS de Lyon / Salle de Conseils (site Monod 2nd floor)
Title: How accurate are the detected communities in Real Networks?

Abstract:
The Internet is composed of routing devices connected between them and organized into independent administrative entities: the Autonomous Systems. The existence of different types of Autonomous Systems (like large connectivity providers, Internet Service Providers or universities) together with geographical and economical constraints, turns the Internet into a complex modular and hierarchical network. This organization is reflected in many properties of the Internet topology, like its high degree of clustering and its robustness.

In this work, we study the modular structure of the Internet router-level graph in order to assess to what extent the Autonomous Systems satisfy some of the known notions of community structure. We show that the modular structure of the Internet is much richer than what can be captured by the current community detection methods, which are severely affected by resolution limits and by the heterogeneity of the Autonomous Systems. Here we overcome this issue by using a multiresolution detection algorithm combined with a small sample of nodes. We also discuss recent work on community structure in the light of our results.

Electro-physiological signal acquisition: Presentation of  experimental conditions and measures.

Graph theory for studying neuro-imaging functional networks

Date and place: Friday Dec. 4 at 9:30am. IXXI conference room (2nd floor).