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^{...
In terms of the spatial dimension, due to the rapid development of graph neural networks, the correlation methods become more diverse. Diverse graph neural network methods are applied by scholars in prediction domain, yet they can generally be divided into two types: spectral domain convolution and...
Subsequently, it integrates graph convolutional neural networks (GCNs) with long short-term memory (LSTM) networks to capture the spatiotemporal characteristics of traffic data. Additionally, a GCN is designed to capture the spatial topological relationships of the road network. Finally, a novel ...
Graph wavelet neural networksSocial media generates vast amounts of spatio-temporal sequential data. However, current methods often ignore the complex spatio-temporal correlations within these data. This oversight makes it difficult to fully capture the dynamic features of the data. To delve deeply ...
[124] developed the single-cell Fluxomics Enrichment Analysis (scFEA) workflow, which utilizes graph neural networks to infer fluxomics information from scRNA-seq data without relying on extracellular flux assumptions. Overall, these investigations contribute insights into the metabolic dynamics at the ...
In Section 2, we introduce related works on graph neural networks and spatio-temporal forecasting. In Section 3, we flesh out the traffic flow prediction problem and present our method in detail. Section 4 describes the dataset, evaluation metrics, etc., and show the experimental results. ...
Anomaly detection Fisher's discriminant function Multi-layer perceptron artificial neural network Operational risk Permanent downhole pressure gauge Spatiotemporal graph 1. Introduction Permanent Downhole Pressure Gauge (PDPG) plays a fundamental role in the obtaining and management of the reservoir characteriz...
Graph neural networks in network neuroscience. IEEE Trans Pattern Anal Mach Intell. 2023;45(5):5833–48. https://doi.org/10.1109/TPAMI.2022.3209686. Article PubMed Google Scholar Ahmedt-Aristizabal D, Armin MA, Denman S, Fookes C, Petersson L. Graph-based deep learning for medical ...
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 ...
Graph convolutional network is motivated via a first-order approximation of spectral graph convolution which aggregates node representations through their neighbours. Specifically, given a graph with adjacency matrix A, and data matrix X, the basic formulation of GCN is to produce an output x′ via...