Here, we show that introducing temporal variation in the network structure can lead to efficient synchronization even when stable synchrony is impossible in any static network under the given budget, thereby demonstrating a fundamental advantage of temporal networks. The temporal networks generated by our...
The hidden geometry of complex, network-driven contagion phenomena. Science 342, 1337–1342 (2013). Article ADS Google Scholar Kumar, J., Rotter, S. & Aertsen, A. Spiking activity propagation in neuronal networks: reconciling different perspectives on neural coding. Nat. Rev. Neurosci. 11, ...
Simultaneously, many existing models are complex in design, expensive in training, and low in usability in real production. Therefore, in this paper, two improved TCN models for time series modeling, especially for renewable power generation time series modeling are applied. Original TCN CRediT ...
Complex networksClustering coefficientNetwork sizeWe propose, discuss and validate a theoretical and numerical framework for sediment-laden, open-channel flows which is based on the two-fluid-model (TFM) equations of motion. The framework models involve mass and momentum equations for both phases (...
[8]), which are easy to interpret, but over-simplify the temporal dynamics of complex actions. Both of these models suffer from the same fundamental issue: intermediate activations are typically a function of the low-level features at the current time step and the state at the previous time ...
The power of any kind of network approach lies in the ability to simplify a complex system so that one can better understand its function as a whole. Sometimes it is beneficial, however, to include more information than in a simple graph of only nodes and links. Adding information about tim...
Traditional modeling methods have complex patterns and are inefficient to capture long-term multivariate dependencies of data for desired forecasting accuracy. To address such concerns, various deep learning models based on Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) methods are...
Li et al., Science 358, 1042–1046 (2017) 24 November 2017 1 of 5 1 Center for Complex Network Research and Department of Physics, Northeastern University, Boston, MA 02115, USA. 2 Center for Systems and Control, College of Engineering, ...
Center for Complex Network Research and Department of Physics, Northeastern University, Boston, MA, 02115, USA Aming Li & Sean P. Cornelius Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, 08544, USA Lei Zhou & Simon A. Levin Department of Mathematics and Depar...
Cliffordet al. took an approach based on using various complex network metrics extracted from climate networks with an LSTM neural network to forecast ENSO phenomena. The preliminary experiments showed that training an LSTM model on a network-metrics time-series data set provides great potential for...