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Assortativity decreases the robustness of interdependent networks

It was recently recognized that interdependencies among different networks can play a crucial role in triggering cascading failures and, hence, systemwide disasters. A recent model shows how pairs of interdependent networks can exhibit an abrupt percolation transition as failures accumulate. We report on the effects of topology on failure propagation for a model system consisting of two interdependent networks. We find that the internal node correlations in each of the two interdependent networks significantly changes the critical density of failures that triggers the total disruption of the two-network system. Specifically, we find that the assortativity (i.e., the likelihood of nodes with similar degree to be connected) within a single network decreases the robustness of the entire system. The results of this study on the influence of assortativity may provide insights into ways of improving the robustness of network architecture and, thus, enhance the level of protection of critical infrastructures.

 

Di Zhou, H. Eugene Stanley, Gregorio D’Agostino and Antonio Scala

"Assortativity decreases the robustness of interdependent networks"

Phys. Rev. E 86, 066103 (2012)

http://link.aps.org/doi/10.1103/PhysRevE.86.066103

 

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The ultimate guide to memory - New Scientist

We are all collections of memories. They dictate how we think, act and make decisions, and even define our identity.

Yet memory, with its many virtues and flaws, has puzzled for centuries. How are memories made and stored in the brain? Why do we remember some events but not others? What do other animals remember? And how can we improve the flawed instrument handed to us by evolution?

In these articles we answer these questions and many more, starting with a revolutionary new understanding of memory’s purpose.


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Complex dynamics of elementary cellular automata emerging in chaotic rules

Complex dynamics of elementary cellular automata emerging in chaotic rules | Papers | Scoop.it

We show novel techniques of analysing complex dynamics of cellular automata (CA) with chaotic behaviour. CA are well known computational substrates for studying emergent collective behaviour, complexity, randomness and interaction between order and disorder. A number of attempts have been made to classify CA functions on their spatio-temporal dynamics and to predict behavior of any given function. Examples include mechanical computation, lambda and Z-parameters, mean field theory, differential equations and number conserving features. We propose to classify CA based on their behaviour when they act in a historical mode, i.e. as CA with memory. We demonstrate that cell-state transition rules enriched with memory quickly transform a chaotic system converging to a complex global behaviour from almost any initial condition. Thus in just a few steps we can select chaotic rules without exhaustive computational experiments or recurring to additional parameters. We provide analysis of well-known chaotic functions in one-dimensional CA, and decompose dynamics of the automata using majority memory.

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