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Graph condensation: A survey
The rapid growth of graph data poses significant challenges in storage, transmission, and
particularly the training of graph neural networks (GNNs). To address these challenges …
particularly the training of graph neural networks (GNNs). To address these challenges …
Towards lossless dataset distillation via difficulty-aligned trajectory matching
The ultimate goal of Dataset Distillation is to synthesize a small synthetic dataset such that a
model trained on this synthetic set will perform equally well as a model trained on the full …
model trained on this synthetic set will perform equally well as a model trained on the full …
Gc-bench: An open and unified benchmark for graph condensation
Graph condensation (GC) has recently garnered considerable attention due to its ability to
reduce large-scale graph datasets while preserving their essential properties. The core …
reduce large-scale graph datasets while preserving their essential properties. The core …
Acceleration algorithms in gnns: A survey
Graph Neural Networks have demonstrated remarkable effectiveness in various graph-
based tasks, but their inefficiency in training and inference poses significant challenges for …
based tasks, but their inefficiency in training and inference poses significant challenges for …
Rethinking and accelerating graph condensation: A training-free approach with class partition
The increasing prevalence of large-scale graphs poses a significant challenge for graph
neural network training, attributed to their substantial computational requirements. In …
neural network training, attributed to their substantial computational requirements. In …
Epidemiology-aware neural ode with continuous disease transmission graph
Effective epidemic forecasting is critical for public health strategies and efficient medical
resource allocation, especially in the face of rapidly spreading infectious diseases. However …
resource allocation, especially in the face of rapidly spreading infectious diseases. However …
Gcondenser: Benchmarking graph condensation
Large-scale graphs are valuable for graph representation learning, yet the abundant data in
these graphs hinders the efficiency of the training process. Graph condensation (GC) …
these graphs hinders the efficiency of the training process. Graph condensation (GC) …
Ameliorate spurious correlations in dataset condensation
Dataset Condensation has emerged as a technique for compressing large datasets into
smaller synthetic counterparts, facilitating downstream training tasks. In this paper, we study …
smaller synthetic counterparts, facilitating downstream training tasks. In this paper, we study …
Disentangled condensation for large-scale graphs
Graph condensation has emerged as an intriguing technique to save the expensive training
costs of Graph Neural Networks (GNNs) by substituting a condensed small graph with the …
costs of Graph Neural Networks (GNNs) by substituting a condensed small graph with the …
GDeR: Safeguarding Efficiency, Balancing, and Robustness via Prototypical Graph Pruning
Training high-quality deep models necessitates vast amounts of data, resulting in
overwhelming computational and memory demands. Recently, data pruning, distillation, and …
overwhelming computational and memory demands. Recently, data pruning, distillation, and …