Adapting Precomputed Features for Efficient Graph Condensation
Authors: Yuan Li, Jun Hu, Zemin Liu, Bryan Hooi, Jia Chen, Bingsheng He
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments show that our approach achieves comparable or better performance while being 96 to 2,455 faster than SOTA methods, making it more practical for large-scale GNN applications. |
| Researcher Affiliation | Collaboration | 1National University of Singapore 2Zhejiang University 3Grab Taxi Holdings Pte. Ltd.. |
| Pseudocode | No | The paper describes the methodology in prose and mathematical equations across sections 3.1, 3.2, and 3.3, but does not include any explicitly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | Yes | Our code and data are available at https://github. com/Xtra-Computing/GCPA. |
| Open Datasets | Yes | our experiments are conducted on seven benchmark datasets including three smaller networks: Cite Seer, Cora, and Pub Med (Kipf & Welling, 2016), and four larger graphs: Ogbn-arxiv, Ogbnproducts (Hu et al., 2020), Flickr (Zeng et al., 2020), and Reddit (Hamilton et al., 2017). |
| Dataset Splits | Yes | We use the public data splits for fair comparisons. The dataset statistics and settings are detailed in Table 1. ... Table 1: Summary of dataset statistics. Setting Dataset # Train/Val/Test Nodes ... Cite Seer 120/500/1,000 ... Cora 140/500/1,000 ... Pub Med 60/500/1,000 ... Ogbn-arxiv 90,941/29,799/48,603 ... Ogbn-products 196,615/39,323/2,213,091 ... Flickr 44,625/22,312/22,313 ... Reddit 153,431/23,831/55,703. |
| Hardware Specification | Yes | The experiments are conducted on a single NVIDIA H100 GPU (80GB). |
| Software Dependencies | No | The paper mentions the use of GCN and SGC as backbone models, and the AdamW optimizer with its settings (learning rate η = 0.001, β1 = 0.9, β2 = 0.999, and λ = 0.01), but it does not specify version numbers for any software libraries, frameworks (e.g., PyTorch, TensorFlow), or programming languages used for implementation. |
| Experiment Setup | Yes | For our method, we tune the structure-based precomputation hops K {1, 2, 3, 4}, damping factor α {0, 0.25, 0.5, 0.75}, residual coefficient β {0, 0.25, 0.5, 0.75}, diversity coefficient γ {0, 0.001, 0.01, 0.1, 1}, semantic-based aggregation size M {1, 10, 50, 100}, number of negative samples S {1, 5, 10, 50}, number of adaptation layers {1, 2, 3}, and hidden dimension of the adaptation module {128, 256, 512}. We tune all hyperparameters on the validation set. We adopt the default settings of Adam W, including learning rate η = 0.001, β1 = 0.9, β2 = 0.999, and λ = 0.01 for weight decay. |