Nested Graph Neural Networks
Authors: Muhan Zhang, Pan Li
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | 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. |