Taken together Eqs. (5) and (6) define a message passing round that propagates information one hop further than the previous round. For instance, one of the very first graph neural networks, namely the graph convolutional network57, proposed the following functions: $$\begin{aligned}&M_r^{...
A scalable graph convolutional deep learning architecture (GCDLA) motivated by the localized first-order approximation of spectral graph convolutions, leverages the extracted temporal features to forecast the wind speed time series of the whole graph nodes. The proposed GCDLA captures spatial wind ...
(iii) What factors lead to changes in metabolites, including the status of enzyme systems that control their synthesis and degradation, as well as the regulatory networks they form? (iv) Considering that cancer-immune interactions involve various cell subsets, how are these metabolites, pathways, a...
CONVOLUTIONAL neural networksAIR pollutantsARTIFICIAL neural networksDIRECTED graphsUNDIRECTED graphsCANONICAL correlation (Statistics)CITIES & townsIn response to the problem that current multi-city multi-pollutant prediction methods based on one-dimensional undirected graph neural network ...
A comprehensive survey on graph neural networks. IEEE Trans. Neural Netw. Learn. Syst. 32, 4–24 (2021). Article MathSciNet Google Scholar Choy, C., Gwak, J. & Savarese, S. 4D spatio-temporal ConvNets: Minkowski convolutional neural networks. In Proc. IEEE/CVF Conference on Computer ...
Make Graph Neural Networks Great Again: A Generic Integration Paradigm of Topology-Free Patterns for Traffic Speed Prediction. Yicheng Zhou, Pengfei Wang, Hao Dong, Denghui Zhang, Dingqi Yang, Yanjie Fu, Pengyang Wang. IJCAI 2024 [link] SCAT: A Time Series Forecasting with Spectral Central Alte...
21-03-13 StemGNN🌟🔥 NIPS 2020 Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting StemGNN 22-05-16 TPGNN NIPS 2022 Multivariate Time-Series Forecasting with Temporal Polynomial Graph Neural Networks TPGNN 22-06-18 D2STGNN VLDB 2022 Decoupled Dynamic Spatial-Temporal ...
It can include filtering approaches such as fusing information within a local window using methods such as interpolation [13, 14], maximum a posteriori (MAP), Bayesian model, Markov random fields (MRFs), and Neural Networks (NN) [4, 12, 15, 16, 17, 18]. Although spatial-spectral fusion...
21-03-13 StemGNN🌟 NIPS 2020 Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting StemGNN 22-05-16 TPGNN NIPS 2022 Multivariate Time-Series Forecasting with Temporal Polynomial Graph Neural Networks TPGNN 22-06-18 D2STGNN VLDB 2022 Decoupled Dynamic Spatial-Temporal Graph...
The cerebral cortex contains diverse neural representations of the visual scene, each enabling distinct visual and spatial abilities. However, the extent to which representations are distributed or segregated across cortical areas remains poorly understo