arXiv Machine Learning
Budgeted Task-Aware Acquisition of Dynamic Networks
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CacheDyG: Decoupling Temporal Propagation for Efficient Dynamic Graph Learning
CacheDyG introduces a cache‑refine framework that decouples temporal propagation from parameter updates in dynamic graph neural networks. By storing graph‑aware node‑time representations in non‑trainable buffers and updating only a lightweight refiner, residual gate, and link predictor during training, it reduces repeated recomputation of historical structures. Experiments on five benchmarks show that CacheDyG uses fewer trainable parameters, runs faster, and achieves competitive or better predictive performance compared to existing baselines.
arXiv Machine Learning
Jul 7
FLASH: Flexible Learning of Adaptive Sampling from History in Temporal Graph Neural Networks
arXiv:2504. 07337v2 Announce Type: replace Abstract: Aggregating temporal signals from historic interactions is a key step in future link prediction on dynamic graphs.
arXiv Machine Learning
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A Lightweight Plastic-Memory Framework for Graph Few-Shot Class-Incremental Learning
arXiv:2609.25781v1 Announce Type: new Abstract: Graph Incremental Learning has garnered increasing attention as dynamic graph data continues to emerge across diverse fields. Conventional approaches p...
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Online Test-Time Adaptation for Generalizable Dynamic Graph Anomaly Detection
Generalizable dynamic graph anomaly detection (DGAD) enables pretrained detectors to identify anomalies in unseen target domains without costly retraining. However, existing methods often fail for two reasons.
arXiv Machine Learning
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SiST-GNN: Simultaneous Spatial-Temporal Message Passing for Dynamic Graph Representation Learning
SiST‑GNN introduces a simultaneous spatial‑temporal message‑passing framework for dynamic graph neural networks, fusing per‑node temporal embeddings with spatial aggregation in a single operation. By maintaining a recurrent hidden state per node and treating it as a cross‑time edge, the model jointly reasons over topology and evolution. Experiments on link‑prediction and node‑classification benchmarks show significant improvements over prior methods, achieving up to 158% gains in live‑update link prediction and outperforming discrete‑time baselines by 7–23% in dynamic node classification.
arXiv Machine Learning
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PreGress: Ranking-Native Pre-training and Prompting for Graph Node Ranking
arXiv:2608. 09016v1 Announce Type: cross Abstract: Node ranking is a fundamental problem in graph information retrieval, measuring the relative importance of nodes and supporting a wide range of applications such as influence analysis, recommendation, and graph-based retrieval augmented generation.