Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
EXACT: Scalable Graph Neural Networks Training via Extreme Activation Compression
Authors: Zirui Liu, Kaixiong Zhou, Fan Yang, Li Li, Rui Chen, Xia Hu
ICLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 EXPERIMENTS The experiments are designed to answer the following research questions. RQ1: How effective is EXACT in terms of model performance at different compression rates (Section 5.1)? RQ2: Is the training process of deeper GNNs also robust to the noise introduced by EXACT (Section 5.1)? RQ3: How sensitive is EXACT to its key hyperparameters (Appendix I.4)? RQ4: What is the running time overhead of EXACT (Section 5.2)? RQ5: Is the convergence speed of GNNs impacted by EXACT (Appendix I.3)? RQ6: To what extent can EXACT reduce the hardware requirement for training GNNs on large graphs (Section 5.3)? |
| Researcher Affiliation | Collaboration | Zirui Liu1, Kaixiong Zhou1, Fan Yang1, Li Li2, Rui Chen2 , and Xia Hu1 1Rice University, 2Samsung Research America |
| Pseudocode | Yes | Algorithm 1: Forward Pass of the lth GCN layer |
| Open Source Code | Yes | The code is available at https://github.com/warai-0toko/Exact. |
| Open Datasets | Yes | To evaluate the scalability of EXACT, we adopt five common large-scale graph benchmark datasets from different domains. Namely, Reddit, Flickr, Yelp, ogbn-arxiv, and ogbn-products. ... Table 10: Dataset Statistics. (contains URLs for each dataset) |
| Dataset Splits | Yes | We report the test accuracy associated with the highest validation score. |
| Hardware Specification | Yes | To answer RQ4, we compare the training throughput of EXACT with the baseline using a single RTX 3090 (24GB) GPU. ... train a full-batch Graph SAGE on ogbn-products on a single GTX 1080 Ti with 11GB memory |
| Software Dependencies | Yes | Table 9: Package configurations of our experiments. Package Version CUDA 11.1 pytorch sparse 0.6.12 pytorch scatter 2.0.8 pytorch geometric 1.7.2 pytorch 1.9.0 OGB 1.3.1 |
| Experiment Setup | Yes | G. EXPERIMENT SETTINGS ... G.2 MODEL HYPERPARAMETER CONFIGURATIONS OF TABLE 1 AND TABLE 2 ... Table 11: Training configuration of Full-Batch GCN, Graph SAGE, and GAT in Table 1 and Table 2. ... Table 13: Training configuration of Cluster-GCN in Table 3. |