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..
From Stars to Subgraphs: Uplifting Any GNN with Local Structure Awareness
Authors: Lingxiao Zhao, Wei Jin, Leman Akoglu, Neil Shah
ICLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our method sets new state-of-the-art performance by large margins for several well-known graph ML tasks; specifically, 0.08 MAE on ZINC, 74.79% and 86.887% accuracy on CIFAR10 and PATTERN respectively. ... We conduct extensive experiments on 4 simulation datasets and 5 well-known real-world graph classification & regression benchmarks (Dwivedi et al., 2020; Hu et al., 2020), to show significant and consistent practical benefits of our approach across different MPNNs and datasets. |
| Researcher Affiliation | Collaboration | Lingxiao Zhao Carnegie Mellon Uni. EMAIL Wei Jin Michigan State Uni. EMAIL Leman Akoglu Carnegie Mellon Uni. EMAIL Neil Shah Snap Inc. EMAIL |
| Pseudocode | No | The paper describes its method using equations and textual explanations, along with diagrams, but does not provide a formal pseudocode block or algorithm listing. |
| Open Source Code | Yes | Our implementation is easy-to-use, and directly accepts any GNN from Py G (Fey & Lenssen, 2019) for plug-and-play use. See code at https://github.com/GNNAs Kernel/GNNAs Kernel. |
| Open Datasets | Yes | Simulation Datasets: 1) EXP (Abboud et al., 2021) ... 2) SR25 (Balcilar et al., 2021) ... Large Real-world Datasets: ZINC-12K, CIFAR10, PATTER from Benchmarking GNNs (Dwivedi et al., 2020) and Mol HIV, and Mol PCBA from Open Graph Benchmark (Hu et al., 2020). Small Real-world Datasets: MUTAG, PTC, PROTEINS, NCI1, IMDB, and REDDIT from TUDatset (Morris et al., 2020a). |
| Dataset Splits | Yes | Table 5: Dataset statistics. ... ZINC-12K ... 10000 / 1000 / 1000 ... CIFAR10 ... 45000 / 5000 / 10000 ... PATTERN ... 10000 / 2000 / 2000 ... Mol HIV ... 32901 / 4113 / 4113 ... Mol PCBA ... 350343 / 43793 / 43793 ... To reduce the search space, we search hyperparameters in a two-phase approach: First, we search common ones (hidden size from [64, 128], number of layers L from [2,4,5,6], (sub)graph pooling from [SUM, MEAN] for each dataset using GIN based on validation performance, and fix it for any other GNN and GNN-AK(+). |
| Hardware Specification | Yes | All experiments are conducted on RTX-A6000 GPUs. |
| Software Dependencies | No | The paper mentions 'directly accepts any GNN from Py G (Fey & Lenssen, 2019)' but does not specify version numbers for PyG or any other software components like Python or PyTorch. |
| Experiment Setup | Yes | To reduce the search space, we search hyperparameters in a two-phase approach: First, we search common ones (hidden size from [64, 128], number of layers L from [2,4,5,6], (sub)graph pooling from [SUM, MEAN] for each dataset using GIN based on validation performance, and fix it for any other GNN and GNN-AK(+). ... We use Batch Normalization and Re LU activation in all models. For optimization we use Adam with learning rate 0.001 and weight decay 0. |