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 [1].
FASTRAIN-GNN: Fast and Accurate Self-Training for Graph Neural Networks
Authors: Amrit Nagarajan, Anand Raghunathan
TMLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | On few-shot node classification tasks using different GNN architectures, FASTRAIN-GNN produces models that are consistently more accurate (by up to 4.4%), while also substantially reducing the self-training time (by up to 2.1 ) over the current state-of-the-art methods. ... 4 Experiments and Results ... Table 2: Results of training GCN with different label rates. |
| Researcher Affiliation | Academia | Amrit Nagarajan EMAIL School of Electrical and Computer Engineering Purdue University Anand Raghunathan EMAIL School of Electrical and Computer Engineering Purdue University |
| Pseudocode | Yes | Algorithm 1: Sampling-based Pseudolabel Filtering (SPF) ... Algorithm 2: Dynamic Regularization (DR) and Dynamic Sizing (DS) ... Algorithm 3: Progressive Graph Pruning (PGP) |
| Open Source Code | Yes | Code is available at https://github.com/amrnag/FASTRAIN-GNN. |
| Open Datasets | Yes | The datasets used for testing are summarized in Table 1. [Listing Cora, Citeseer, Pubmed, Cora Full]. ... We present results on the Chameleon, Texas, Wisconsin and Cornell datasets with different label rates in Table 12. |
| Dataset Splits | Yes | We randomly select (labels/class) nodes of each class as training nodes, and report results on the rest of the nodes in the graph (we do not require a separate held-out validation set for any of the FASTRAIN-GNN optimizations). We repeat this process 100 times for each value of (labels/class), and all results reported in this section are averaged across 100 different training splits (with error bars indicating accuracy range), unless otherwise specified. |
| Hardware Specification | Yes | We implement FASTRAIN-GNN using DGL in Py Torch, and evaluate it on a Ge Force RTX 2080 Ti GPU with 11GB memory. |
| Software Dependencies | No | We implement FASTRAIN-GNN using DGL in Py Torch. The paper mentions DGL and Py Torch but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | The details of the hyperparameters used in all experiments are presented in Appendix D. ... Table 8: Hyperparameters used in our experiments. The exact same hyperparameters are used in all our experiments spanning different datasets, GNN architectures and label rates. ... 4 stages of self-training are performed, with 500 epochs of training in each stage in all methods. |