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 COMPLEX NETWORKS 2016 The 5th International Workshop on Complex Networks
and their Applications, MILAN November 30 –December 02, 2016

*TUTORIALS: NOVEMBER 29, 2016* - tutorial


Please use the following link:

*Registration reminders: *

·       Please note that you do not need to register for the workshop to
register for the tutorials.

·       Registration is on a first-come, first-served basis and is limited
to 20 participants per tutorial. So please, register as soon as possible
and preferably *before October 25, 2016*.

·       It will be possible to register to tutorials after that date,
provided that capacity constraints are not already violated.


*Consensus dynamics on networks. Theory and applications*


Consensus is well documented across the social sciences, with examples
ranging from behavioral flocking in popular cultural styles, emotional
contagion, collective decision making, pedestrians’ walking behavior, and
others. We can model consensus in a social group by encoding the state of
each individual at a given time in a vector. The group reaches consensus at
when the difference in the “opinions” for every pair of individuals is
asymptotically zero, and the collective dynamics of the system is modeled
by a diffusion equation dominated by the graph Laplacian. Decisions in
groups trying to reach consensus are frequently influenced by a small
proportion of the group who guides or dictates the behavior of the entire
network. In this situation a group of leaders indicates and/or initiates
the route to the consensus, and the rest of the group readily follows their
attitudes. The study of leadership in social groups has always intrigued
researchers in the social and behavioral sciences. Specifically, the way in
which leaders emerge in social groups is not well understood. Leaders may
emerge either randomly in response to particular historical circumstances
or from the individual having the most prominent position (centrality) in
the social network at any time. In this tutorial I will introduce the
theoretical model of consensus in a network, for the general case of
undirected as well as directed ones. First, I will introduce the
mathematical concepts of the model, and show when in every case there is a
consensus in the network. I will also introduce some properties of the
Laplacian matrix for networks that will help to understand the main results
of the model. Then, I will introduce a controllability problem and its
solution in networks consisting of leaders and followers. Following this
initial part I will how to use Matlab to model a consensus process in a
given network (codes will be provided to participants). At this point I
will motive the necessity of considering the indirect influence of peers
apart from the direct peers pressure. In mathematical terms I will make a
generalization of the Laplacian matrix on graphs to consider the k-path
Laplacians and their transform. Using this transformed k-path Laplacians I
will show how to study a few interesting topics on networks, such as the
controllability of networks, the selection of leaders, the diffusion of
innovations under direct+indirect peers pressure. Finally, I will prove and
illustrate how the consensus and diffusion of innovations can be
superdiffusive or ballistic in complex networks under the effect of direct
and indirect peers pressure. Some examples, such as diffusion of methods
among high schools or the adoption of a biotechnological product among
farmers will be used in the tutorial.

*Professor Estrada has an internationally leading reputation for shaping
and developing the study of complex networks. His expertise ranges in the
areas of network structure, algebraic network theory, dynamical systems on
networks and the study of random models of networks. He has a distinguished
track record of high-quality publications, which has attracted more than 8,
500 citations. His h-index (number of papers with at least h citations) is
53. His publications are in the areas of network theory and its
applications to social, ecological, engineering, physical, chemical and
biological real-world problems. Professor Estrada has published two text
books on network sciences both published by Oxford University Press in 2011
and 2015, respectively. He has demonstrated a continuous international
leadership in his field where he has been invited and plenary speaker at
the major conferences in network sciences and applied mathematics. His
research interests include the use of matrix functions; random geometric
networks; generalised Laplacian operators for networks; generalised
diffusion models for networks; study of indirect peer pressure over
consensus dynamics on networks; applications of network sciences to oil and
gas exploration; spatial efficiency of networks; Euclidean geometrical
embedding of networks, among many others.*


*A pr**actical introduction to Machine **Learning (with Python)*


The data deluge we currently witnessing presents both opportunities and
challenges. Never before have so many aspects of our world been so
thoroughly quantified as now and never before has data been so plentiful.
On the other hand, the complexity of the analyses required to extract
useful information from these piles of data is also rapidly increasing
rendering more traditional and simpler approaches simply unfeasible or
unable to provide new insights.

In this tutorial we provide a practical introduction to some of the most
important algorithms of machine learning that are relevant to the field of
Complex Networks in general, with a particular emphasis on the analysis and
modeling of empirical data. The goal is to provide the fundamental concepts
necessary to make sense of the more sophisticated data analysis approaches
that are currently appearing in the literature and to provide a field guide
to the advantages an disadvantages of each algorithm.
In particular, we will cover unsupervised learning algorithms such as
K-means, Expectation-Maximization, and supervised ones like Support Vector
Machines, Neural Networks and Deep Learning. Participants are expected to
have a basic understanding of calculus and linear algebra as well as
working proficiency with the Python programming language.

*Bruno Gonçalves is a Data Science fellow at NYU's Center for Data Science
while on leave from a tenured faculty position at Aix-Marseille Université.
He has a strong expertise in using large scale datasets for the analysis of
human behavior. After completing his joint PhD in Physics, MSc in C.S. at
Emory University in Atlanta, GA in 2008 he joined the Center for Complex
Networks and Systems Research at Indiana University as a Research
Associate. From September 2011 until August 2012 he was an Associate
Research Scientist at the Laboratory for the Modeling of Biological and
Technical Systems at Northeastern University. Since 2008 he has been
pursuing the use of Data Science and Machine Learning to study human
behavior. By processing and analyzing large datasets from Twitter,
Wikipedia, web access logs, and Yahoo! Meme he studied how we can observe
both large scale and individual human behavior in an obtrusive and
widespread manner. The main applications have been to the study of
Computational Linguistics, Information Diffusion, Behavioral Change and
Epidemic Spreading. He is the author of 60+ publications with over 3800+
Google Scholar citations and an h-index of 26. In 2015 he was awarded the
Complex Systems Society's 2015 Junior Scientific Award for "outstanding
contributions in Complex Systems Science" and he is the editor of the book
Social Phenomena: From Data Analysis to Models (Springer, 2015).*


*Join us at *:  COMPLEX NETWORKS 2016 Milan <>

*Publish your work on:* Applied Network Science
Networks & their Applications
*   Pr Hocine CHERIFI                             *
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