Graph Retention Networks extend retention to dynamic graphs to enable parallelizable training, O(1) inference, and chunkwise long-term training while delivering competitive performance with major efficiency gains.
Do we really need complicated model architectures for temporal networks?
3 Pith papers cite this work. Polarity classification is still indexing.
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DSRD unifies temporal and structural adaptation for dynamic graphs via a single recurrent retentive state with learnable time-sensitivity parameters in the decay kernels.
A hybrid quantum-classical temporal graph network with adaptive amplitude encoding claims strong link-prediction results on five TGBL benchmarks, but the evaluation is undermined by a below-random baseline and missing code.
citing papers explorer
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Graph Retention Networks for Dynamic Graphs
Graph Retention Networks extend retention to dynamic graphs to enable parallelizable training, O(1) inference, and chunkwise long-term training while delivering competitive performance with major efficiency gains.
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Forget Less, Generalize More: Unifying Temporal and Structural Adaptation for Dynamic Graphs
DSRD unifies temporal and structural adaptation for dynamic graphs via a single recurrent retentive state with learnable time-sensitivity parameters in the decay kernels.
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A2QTGN: Adaptive Amplitude Quantum-Integrated Temporal Graph Network for Dynamic Link Prediction
A hybrid quantum-classical temporal graph network with adaptive amplitude encoding claims strong link-prediction results on five TGBL benchmarks, but the evaluation is undermined by a below-random baseline and missing code.