Temporal Graph Neural Networks
PulseAugur coverage of Temporal Graph Neural Networks — every cluster mentioning Temporal Graph Neural Networks across labs, papers, and developer communities, ranked by signal.
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New method boosts temporal graph neural networks with motif signatures
Researchers have developed a new method to enhance temporal graph neural networks (TGNNs) by incorporating temporal motif signatures. These signatures capture predictive patterns like repetition and reciprocity within i…
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New HERMIT Framework Uses Hyperbolic Geometry for Internet Latency Prediction
Researchers have developed HERMIT, a novel framework for predicting Internet latency and routing dynamics. HERMIT utilizes hyperbolic geometry to better represent the scale-free structure of Internet routing graphs, out…
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TGFormer architecture enhances temporal graph analysis with auto-correlation
Researchers have introduced TGFormer, a new Transformer architecture designed to improve the modeling of temporal graphs. This model addresses limitations in capturing long-term dependencies and identifying periodic pat…
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New method disentangles stability and transition patterns for TGNN interpretability
Researchers have introduced ST-TGExplainer, a novel method designed to improve the interpretability of Temporal Graph Neural Networks (TGNNs). Existing models often struggle to distinguish between the influence of past …
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Researchers develop Shapley value explainers for temporal graph neural networks
Researchers have developed two new model-agnostic explainers for Temporal Graph Neural Networks (TGNNs), utilizing Shapley and Owen values. These methods aim to make the predictions of TGNNs, which combine spatial and t…