The workshop will be held from the 25th until the 29th of January 2016. It is expected that participants take part into the workshop activities from 10.00 AM until 6.30 PM every day. The workshop will have also a social dinner (already included in the workshop registration fee).
Participants can also be involved into two pre-workshop social events before the official start: a mountain hike and a Madrid visiting tour. Please get in touch with the Steering Committee for further info about these two social events.
A certificate of attendance will be given to those participants who have engaged in the workshop activities.
The updated schedule for WWCS2016 is the following:
Participants will have the opportunity to give tutorials in their fields.
Tutorial 1 – Tuesday 26 – 30 min slot – from 13:00 to 13:30 – Laura Alessandretti
Visualisation of geo-localised data.
Many real-world complex systems are embedded in the geographical space. Examples include transportation systems, social systems, road networks, cities, and the Internet. The availability of rich geo-localised data and accessible tools allow for increasing opportunities in understanding spatially-embedded socio-technical systems. The goal of this tutorial is to introduce the attendants to the use of some open-source free tools for the analysis and visualization of geo-localised data. I will propose an exercise starting from shared-bicycles trips data. First, we will perform simple measurements using Python tools. Then, I will show how to use the open-source software QGIS for visualizing the results on maps.
Tutorial 2 – Wednesday 27 – 30 min slot – from 12:30 to 13:00 – Jan Haerter
The interplay of bistability and noise in interacting networks of epigenetic states.
Epigenetics describes phenotypical changes that come without changes to the DNA nucleotide sequence itself. Epigenetics can e.g. manifest itself in DNA methylation, i.e. chemical binding of a methyl group to the cytosine nucleotide within the cytosine-guanine dinucleotide sequence C-G. These C-G sites are found to cluster in the genome into regions of high densities of C-G sites, termed C-G islands. Interestingly, The C-G’s within C-G islands are often found to be either all methylated or all unmethylated, while intermediate configurations are rare, suggesting a bistable mechanism. Indeed, biological evidence points to interactions, where the methylation state at one C-G site can influence that at another, implying a spatially interacting system of sites, most generally described as a network. Importantly, C-G islands are frequently found in the proximity of gene promotors and their methylation state has been associated with gene expression. We will discuss how bistability can be brought about, and how noise, always present in biological systems, can cause transitions in such correlated systems.
Tutorial 3 – Wednesday 27 – 30 min slot – from 13:00 to 13:30 – Hyunju Kim
Constructing and Sampling Graphs with Arbitrary Degree Sequence.
Degree-based graph construction and sampling is a ubiquitous problem in network modeling, ranging from social sciences to biochemical reaction networks in the cell. This problem includes existence, enumeration, exhaustive construction and sampling questions with aspects that are still open today. To answer those questions, it is crucial to construct all possible simple graphs with a given arbitrary degree sequence.
In this tutorial, I will present my theorem and algorithm that can construct all possible labeled graphs with a given degree sequence. I will also introduce an algorithm developed based on my theorem, which samples graphs with known weights that allows us to do effective uniform sampling in terms of computing average of network observables.
 Hyunju Kim, Zoltán Toroczkai, Péter L Erdös, István Miklós and László A Székely. Degree-
based graph construction. J. Phys. A: Math. Theor. (Fast Track Communication) 42, 392001 (2009).
 Charo I. Del Genio, Hyunju Kim, Zoltán Toroczkai, Kevin E. Bassler. Efficient and exact sampling of simple graphs with given arbitrary degree sequence. PLoS ONE, 5(4), e10012, (2010).
 Hyunju Kim, Charo I. Del Genio, Kevin E. Bassler, and Zoltán Toroczkai. Constructing and sampling directed graphs with given degree sequence. New J. Phys. 14, 023012 (2012).
Tutorial 4 – Thursday 28 – 30 min slot – from 11.30 to 12.00 – Sabin Roman
Modelling the macro-dynamics and collapse of societies: theory, examples and lessons.
The tutorial will start by giving a short overview of the theory of societal collapse and the driving mechanisms for the rise of complexity in societies. The theory behind collapse is not well known but gives genuine insight on general complex systems. Some important modelling examples will be given that have similar aims in terms of the modelling objective but very different methodologies. These are meant to give a practical flavour for the modelling and an IPython notebook is provided that implements the models using the PyDSTool. The simplest way to get everything you need to run the notebook is to install the Anaconda distribution (Python 2.7). Finally, lessons are drawn that highlight the best modelling practices, especially when dealing with social systems and the use of differential equations.
Tutorial 5 – Thursday 28 – 30 min slot – from 12.00 to 12.30 – Nikos E. Kouvaris
Pattern formation on network-organised reaction-diffusion systems.
Distributed active media are found in a wide range of natural systems including neural cells, heart tissue, surface chemical reactions, ecological and social systems, etc. They consist of coupled elements obeying an activator-inhibitor dynamics. Such media were broadly studied with continuous reaction-diffusion equations and support a variety of self-organized spatio-temporal patterns like Turing patterns, traveling and stationary fronts, synchronization, rotating spirals, etc.
Within the last decade, self-organization of patterns has been considered in networks, where reactions occur on the network’s nodes and diffusion is carried out through the links connecting them. Such systems can be formed by diffusively coupled chemical reactors, biological cells or dispersal habitats. The rapid development in network science provides increasing insight into the impact of their topology upon the emerging collective dynamics. A variety of self-organizing phenomena has been studied in such complex systems including epidemic spreading, synchronization and chimera states, stationary Turing and self-organized oscillatory patterns, etc. Collective phenomena induced by feedback control or by noise have also been analyzed in networks.
Here we attempt to discuss methods and tools for the analysis and visualization of those self-organized patterns by considering some classical examples of active networks. For this purpose we are going to use IPython Notebook for interactive computing and make use of the packages: scipy, numpy, matplotlib, networkx, multinetx (https://github.com/nkoub/