🚩 http://indico.ictp.it/event/8117/

🎯 "What are Lyapunov Exponents, and Why are they Interesting?" by Amie Wilkinson

💡 "Complex Systems Studies" is a graduate-level channel aiming to discuss all kinds of stuff related to the field of Complex Systems.

🗺 Our purpose is to be up-to-date, precise and international.

✔️ Introduce us to your friends and colleagues at all over the globe:
https://telegram.me/ComplexSys

📄 Higher-order organization of complex networks

Austin R. Benson, David F. Gleich, Jure Leskovec

🔗 https://arxiv.org/pdf/1612.08447v1

📌 ABSTRACT
Networks are a fundamental tool for understanding and modeling complex systems in physics, biology, neuroscience, engineering, and social science. Many networks are known to exhibit rich, lower-order connectivity patterns that can be captured at the level of individual nodes and edges. However, higher-order organization of complex networks---at the level of small network subgraphs---remains largely unknown. Here we develop a generalized framework for clustering networks based on higher-order connectivity patterns. This framework provides mathematical guarantees on the optimality of obtained clusters and scales to networks with billions of edges. The framework reveals higher-order organization in a number of networks including information propagation units in neuronal networks and hub structure in transportation networks. Results show that networks exhibit rich higher-order organizational structures that are exposed by clustering based on higher-order connectivity patterns.

📽 http://www.ted.com/talks/vs_ramachandran_the_neurons_that_shaped_civilization

📄 Recent history of fractional calculus

http://www.sciencedirect.com/science/article/pii/S1007570410003205

📌Abstract
This survey intends to report some of the major documents and events in the area of fractional calculus that took place since 1974 up to the present date.

#Fractional_calculus

📌 https://www.math.washington.edu/~morrow/336_14/papers/danny.pdf

🎯 http://tex.stackexchange.com/questions/57152/how-to-draw-graphs-in-latex

🎯 https://www.codementor.io/mcorr/tutorials/an-introduction-to-python-machine-learning-with-perceptrons-k7pn85vfi?utm_content=buffer43c78&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer

📽 https://www.youtube.com/watch?v=_m8LhAV8M-M

Speaker:
Emanuela Merelli, University of Camerino

📌 ABSTRACT:
According to Frege’s principle of compositionality, the meaning of a complex expression is determined by the meanings of its constituent expressions and the rules used to combine them. This talk will try to show how this principle acquires new unexpected features in the context of complex systems – which are composed of many non-identical elements, entangled in loops of nonlinear interactions – due to the characteristic 'emergence' effects. Indeed, representing expression composition as a path algebra construct in data space (the space on which composition rules operate) and embedding the latter in a simplicial complex – geometric and at the same time combinatorial notion coming from algebraic topology – provides us with a mathematical structure that naturally allows for a finer classification of compositional rules in equivalence classes not identifiable otherwise.

🔗 https://simons.berkeley.edu/sites/default/files/docs/6000/talk-b-final.pdf

📽 https://video.simons.berkeley.edu/2016/logic/3/22-Merelli.mp4

🌀 https://simons.berkeley.edu/programs/bigdata2013

📄 Motifs in Temporal Networks

Ashwin Paranjape, Austin R. Benson, Jure Leskovec

🔗 https://arxiv.org/pdf/1612.09259v1

📌 ABSTRACT
Networks are a fundamental tool for modeling complex systems in a variety of domains including social and communication networks as well as biology and neuroscience. Small subgraph patterns in networks, called network motifs, are crucial to understanding the structure and function of these systems. However, the role of network motifs in temporal networks, which contain many timestamped links between the nodes, is not yet well understood. 
Here we develop a notion of a temporal network motif as an elementary unit of temporal networks and provide a general methodology for counting such motifs. We define temporal network motifs as induced subgraphs on sequences of temporal edges, design fast algorithms for counting temporal motifs, and prove their runtime complexity. Our fast algorithms achieve up to 56.5x speedup compared to a baseline method. Furthermore, we use our algorithms to count temporal motifs in a variety of networks. Results show that networks from different domains have significantly different motif counts, whereas networks from the same domain tend to have similar motif counts. We also find that different motifs occur at different time scales, which provides further insights into structure and function of temporal networks.

🎯 https://www.datacamp.com/community/tutorials/r-or-python-for-data-analysis#gs.3g8mujU

🎯 http://www.popsci.com/this-book-is-a-complete-guide-to-the-science-of-snowflakes?con=TrueAnthem&dom=tw&src=SOC&utm_campaign=&utm_content=5869d74db8a9fe0007a16152&utm_medium=&utm_source=

🎯 2017 : WHAT SCIENTIFIC TERM OR CONCEPT OUGHT TO BE MORE WIDELY KNOWN?

https://www.edge.org/response-detail/27036

#networks

🎯 My answer to the Edge 2017 annual question: What scientific concept should be better known?

#Criticality

In physics we say a system is in a critical state when it is ripe for a phase transition. Consider water turning into ice, or a cloud that is pregnant with rain. Both of these are examples of physical systems in a critical state.

The dynamics of criticality, however, are not very intuitive. Consider the abruptness of freezing water. For an outside observer, there is no difference between cold water and water that is just about to freeze. This is because water that is just about to freeze is still liquid. Yet, microscopically, cold water and water that is about to freeze are not the same.

When close to freezing, water is populated by gazillions of tiny ice crystals, crystals that are so small that water remains liquid. But this is water in a critical state, a state in which any additional freezing will result in these crystals touching each other, generating the solid mesh we know as ice. Yet, the ice crystals that formed during the transition are infinitesimal. They are just the last straw. So, freezing cannot be considered the result of these last crystals. They only represent the instability needed to trigger the transition; the real cause of the transition is the criticality of the state.

But why should anyone outside statistical physics care about criticality?

The reason is that history is full of individual narratives that maybe should be interpreted in terms of critical phenomena.

Did Rosa Parks start the civil rights movement? Or was the movement already running in the minds of those who had been promised equality and were instead handed discrimination? Was the collapse of Lehman Brothers an essential trigger for the Great Recession? Or was the financial system so critical that any disturbance could have made the trick?

As humans, we love individual narratives. We evolved to learn from stories and communicate almost exclusively in terms of them. But as Richard Feynman said repeatedly: The imagination of nature is often larger than that of man. So, maybe our obsession with individual narratives is nothing but a reflection of our limited imagination. Going forward we need to remember that systems often make individuals irrelevant. Just like none of your cells can claim to control your body, society also works in systemic ways.

So, the next time the house of cards collapses, remember to focus on why we were building a house of cards in the first place, instead of focusing on whether the last card was the queen of diamonds or a two of clubs.

🔗 https://facebook.com/story.php?story_fbid=10154355446216693&id=727621692