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..
IGLU: Efficient GCN Training via Lazy Updates
Authors: S Deepak Narayanan, Aditya Sinha, Prateek Jain, Purushottam Kar, SUNDARARAJAN SELLAMANICKAM
ICLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Benchmark experiments show that IGLU offers up to 1.2% better accuracy despite requiring up to 88% less compute. Section 4, 'Empirical Evaluation', details datasets, baselines, and results, including 'Test accuracies are reported in Table 1 and convergence plots are shown in Figure 2', confirming empirical studies. |
| Researcher Affiliation | Collaboration | S Deepak Narayanan & Aditya Sinha Microsoft Research India EMAIL, Prateek Jain Microsoft Research India EMAIL, Purushottam Kar IIT Kanpur & Microsoft Research India EMAIL, Sundararajan Sellamanickam Microsoft Research India EMAIL |
| Pseudocode | Yes | Algorithm 1 IGLU: backprop order and Algorithm 2 IGLU: inverted order |
| Open Source Code | Yes | An implementation of IGLU can be found at the following URL https://github.com/sdeepaknarayanan/iglu |
| Open Datasets | Yes | The following five benchmark tasks were used: (1) Reddit (Hamilton et al., 2017): (2) PPI-Large (Hamilton et al., 2017): (3) Flickr (Zeng et al., 2020): (4) OGBN-Arxiv (Hu et al., 2020): and (5) OGBN-Proteins (Hu et al., 2020): |
| Dataset Splits | Yes | Training-validation-test splits and metrics were used in a manner consistent with the original release of the datasets: specifically ROC-AUC was used for OGBN-Proteins and micro-F1 for all other datasets. Dataset descriptions and statistics are presented in Appendix B. Table 8 provides details on the benchmark node classification datasets used in the experiments. Train/Val/Test PPI-Large 0.79/0.11/0/10 Reddit 0.66/0.10/0.24 Flickr 0.5/0.25/0.25 OGBN-Proteins 0.65/0.16/0.19 OGBN-Arxiv 0.54/0.18/0.28 |
| Hardware Specification | Yes | We implement IGLU in Tensor Flow 1.15.2 and perform all experiments on an NVIDIA V100 GPU (32 GB Memory) and Intel Xeon CPU processor (2.6 GHz). |
| Software Dependencies | Yes | We implement IGLU in Tensor Flow 1.15.2 and perform all experiments on an NVIDIA V100 GPU (32 GB Memory) and Intel Xeon CPU processor (2.6 GHz). |
| Experiment Setup | Yes | Model Selection and Hyperparameter Tuning. Model selection was done for all methods based on their validation set performance. For IGLU, Graph SAGE and VR-GCN, an exhaustive grid search was done over general hyperparameters such as batch size, learning rate and dropout rate (Srivastava et al., 2014). In addition, method-specific hyperparameter sweeps were also carried out that are detailed in Appendix A.4. IGLU : Learning Rate {0.01, 0.001} with learning rate decay schemes, Batch Size {512, 2048, 4096, 10000}, Dropout {0.0, 0.2, 0.5, 0.7} |