What can we learn from the collective dynamics of a complex network about its interaction topology? Taking the perspective from nonlinear dynamics, we briefly review recent progress on how to infer structural connectivity (direct interactions) from accessing the dynamics of the units. Potential applications range from interaction networks in physics, to chemical and metabolic reactions, protein...

the proliferation of information and communications technology (ict) along with the incremental growth in the use of cloud services, enterprise network and data center technologies in recent years, have caused unprecedented expansion of computer networks. however, the existing network architecture was not well designed to meet the requirements of today's needs. one of the most important pr...

The purpose of this study is designing a model based on Tobit regression, DEA, Artificial Neural Network, Genetic Algorithm and Particle Swarm Optimization to evaluate the efficiency and also benchmarking the efficient and inefficient units. This model has three stages, and it uses the data envelopment analysis combined model with neural network, optimized by genetic algorithm, to evaluate the ...

Credit networks rely on decentralized, pairwise trust relationships (channels) to exchange money or goods. arise naturally in many financial systems, including the recent construct of payment channel blockchain systems. An important performance metric for these is their transaction throughput. However, predicting throughput a credit network nontrivial. Unlike traditional communication channels,...