Substructure Aware Graph Neural Networks
Authors: DingYi Zeng, Wanlong Liu, Wenyu Chen, Li Zhou, Malu Zhang, Hong Qu
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our extensive experiments demonstrate the effectiveness of our framework, achieving state-of-the-art performance on a variety of well-proven graph tasks, and GNNs equipped with our framework perform flawlessly even in 3-WL failed graphs. Specifically, our framework achieves a maximum performance improvement of 83% compared to the base models and 32% compared to the previous state-of-the-art methods. Our extensive experiments validate the state-of-the-art performance of our framework on various base model networks, tasks and datasets, especially on the graph regression task of drug constrained solubility prediction (ZINCFULL). Our framework achieves a maximum MAE reduction of 83% compared to the base model and a maximum MAE reduction of 32% compared to the previous state-of-the-art model. |
| Researcher Affiliation | Academia | 1School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China |
| Pseudocode | No | No pseudocode or algorithm blocks were found explicitly labeled or formatted as such in the paper. |
| Open Source Code | Yes | Our implementation is available at https://github.com/ Black Halo-Drake/SAGNN-Substructure-Aware-Graph-Neural Networks |
| Open Datasets | Yes | We use EXP (Abboud et al. 2021) and SR25 (Balcilar et al. 2021) datasets to verify the expressiveness improvement of our framework for MPNNs, where EXP contains 600 pairs of 1-WL failed graphs and SR25 contains 15 3-WL failed strongly regular graphs. For performance on real-world tasks, we use three kinds of datasets of different scales for validation. The first kind is small-scale real-world datasets TUDataset (Morris et al. 2020), which includes MUTAG, PTC, PROTEINS, NCI1 and IMDB from biology, chemistry and social networks. The second is a large-scale molecular benchmark from the zinc database from the ZINC database (Sterling and Irwin 2015), which includes ZINC (12K graphs) (Dwivedi et al. 2020) and ZINC-FULL (250k graphs) (G omez-Bombarelli et al. 2018; Jin, Barzilay, and Jaakkola 2018; Zhang et al. 2018a). The last is a molecular large-scale dataset from the Open Graph Benchmark (Hu et al. 2020, 2021) which includes OGBG-MOLHIV (41k graphs) and OGBG-PCBA (437k graphs). |
| Dataset Splits | Yes | For TUDataset, we perform 10-fold cross-validation and report the average and standard deviation of validation accuracy across the 10 folds within the cross-validation. |
| Hardware Specification | No | The paper does not explicitly mention specific hardware components (e.g., GPU models, CPU types, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not explicitly list specific software dependencies with their version numbers (e.g., Python version, library versions) needed for replication. |
| Experiment Setup | Yes | Hyperparameter effect We first conduct an ablation study to analyze the impact of the values of Ego and Cut on model performance on different tasks and different data schema. Figure 4 visualizes the results of our hyperparameter experiments on three datasets. All three datasets show the same performance trend, that is, the best performance in the case of Ego=3, Cut=4, which shows our model s great hyperparameter stability. Experiments are repeated 10 times on expressiveness datasets and 3 times on large scale datasets to calculate mean and standard derivation. For TUDataset, we perform 10-fold cross-validation and report the average and standard deviation of validation accuracy across the 10 folds within the cross-validation. |