Temporal Graph Networks combine memory modules and graph operators to learn on dynamic graphs as timed event sequences, outperforming prior methods on transductive and inductive tasks while unifying earlier models as special cases.
Evolvegcn: Evolving graph convolutional networks for dynamic graphs
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Adding MAR climate-model features plus a multi-branch GraphSAGE/temporal-convolution design with adaptive fusion reduces deep-ice-layer thickness RMSE by 21.01% over a no-knowledge multi-branch baseline on the SRED Greenland dataset.
A spectral-based GCN for directed graphs uses redefined Laplacians to enable direct application to directed data and outperforms prior methods on semi-supervised node classification tasks.
citing papers explorer
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Temporal Graph Networks for Deep Learning on Dynamic Graphs
Temporal Graph Networks combine memory modules and graph operators to learn on dynamic graphs as timed event sequences, outperforming prior methods on transductive and inductive tasks while unifying earlier models as special cases.
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K-STEMIT: Knowledge-Informed Spatio-Temporal Efficient Multi-Branch Graph Neural Network for Subsurface Stratigraphy Thickness Estimation from Radar Data
Adding MAR climate-model features plus a multi-branch GraphSAGE/temporal-convolution design with adaptive fusion reduces deep-ice-layer thickness RMSE by 21.01% over a no-knowledge multi-branch baseline on the SRED Greenland dataset.
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Spectral-based Graph Convolutional Network for Directed Graphs
A spectral-based GCN for directed graphs uses redefined Laplacians to enable direct application to directed data and outperforms prior methods on semi-supervised node classification tasks.