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.
1 day(s) with sentiment data
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Machine learning predicts Bitcoin Lightning Network channel closures
Researchers have developed machine learning models to predict channel closures in the Bitcoin Lightning Network. By analyzing two years of network activity, they found that temporal and behavioral features, such as endp…
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FLASH mechanism enhances Temporal Graph Neural Networks performance
Researchers have developed FLASH, a novel mechanism designed to improve the performance of Temporal Graph Neural Networks (TGNNs). FLASH is a learnable and graph-adaptive approach to neighborhood selection, which addres…
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FAST framework accelerates Temporal Graph Neural Network training
Researchers have developed FAST, a new framework designed to optimize the training of Temporal Graph Neural Networks (TGNNs). TGNNs are crucial for analyzing dynamic graphs in areas like recommendations and social netwo…
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New method accelerates temporal GNN training with adaptive pseudo-supervision
Researchers have developed a new method called Moving-Averaged Labels (MAL) to improve the training of temporal graph neural networks (GNNs). This technique addresses the issue of irregular supervision in real-world dyn…
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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…