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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 …
Dataset condensation for recommendation
Training recommendation models on large datasets requires significant time and resources.
It is desired to construct concise yet informative datasets for efficient training. Recent …
It is desired to construct concise yet informative datasets for efficient training. Recent …
Tinygraph: joint feature and node condensation for graph neural networks
Training graph neural networks (GNNs) on large-scale graphs can be challenging due to the
high computational expense caused by the massive number of nodes and high-dimensional …
high computational expense caused by the massive number of nodes and high-dimensional …
Condensing Pre-Augmented Recommendation Data via Lightweight Policy Gradient Estimation
Training recommendation models on large datasets requires significant time and resources.
It is desired to construct concise yet informative datasets for efficient training. Recent …
It is desired to construct concise yet informative datasets for efficient training. Recent …
Random Walk Guided Hyperbolic Graph Distillation
Graph distillation (GD) is an effective approach to extract useful information from large-scale
network structures. However, existing methods, which operate in Euclidean space to …
network structures. However, existing methods, which operate in Euclidean space to …