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..
Nested Graph Neural Networks
Authors: Muhan Zhang, Pan Li
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we study the effectiveness of the NGNN framework for graph classification and regression tasks. In particular, we want to answer the following questions: Q1 Can NGNN reach its theoretical power to discriminate 1-WL-indistinguishable graphs? Q2 How often and how much does NGNN improve the performance of a base GNN? Q3 How does NGNN perform in comparison to state-of-the-art GNN methods in open benchmarks? Q4 How much extra computation time does NGNN incur? We implement the NGNN framework based on the PyTorch Geometric library [51]. Our code is available at https://github.com/muhanzhang/NestedGNN. 5.1 Datasets 5.3 Results and discussion |
| Researcher Affiliation | Academia | Muhan Zhang1,2, Pan Li3, 1Institute for Artificial Intelligence, Peking University 2Beijing Institute for General Artificial Intelligence 3Department of Computer Science, Purdue University |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found in the paper. The methods are described using natural language and mathematical equations. |
| Open Source Code | Yes | Our code is available at https://github.com/muhanzhang/NestedGNN. |
| Open Datasets | Yes | To answer Q2, we use the QM9 dataset [52, 53] and the TU datasets [54]. ... To answer Q3, we use two Open Graph Benchmark (OGB) datasets [59], ogbg-molhiv and ogbg-molpcba. |
| Dataset Splits | Yes | Table 1: Statistics and evaluation metrics of the QM9 and OGB datasets. Split ratio: 80/10/10 and TU. ... we uniformly use the 10-fold cross validation framework provided by PyTorch Geomtric [66] |
| Hardware Specification | No | No specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running the experiments are provided. The paper only vaguely mentions 'GPU memory' in the conclusion without further specifications. |
| Software Dependencies | No | The paper mentions 'PyTorch Geometric library' but does not specify a version number for it or for any other software dependencies such as Python or other libraries. |
| Experiment Setup | Yes | For GNNs, we search the number of message passing layers in {2, 3, 4, 5}. For NGNNs, we similarly search the subgraph height h in {2, 3, 4, 5}... All models have 32 hidden dimensions, and are trained for 100 epochs with a batch size of 128. and For NGNN, we search the subgraph height h in {3, 4, 5}, and the number of layers in {4, 5, 6}. We train the NGNN models for 100 and 150 epochs for ogbg-molhiv and ogbg-molpcba, respectively. |