Can Graph Neural Networks Count Substructures?
Authors: Zhengdao Chen, Lei Chen, Soledad Villar, Joan Bruna
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We then conduct experiments that support the theoretical results for MPNNs and 2-IGNs. We empirically demonstrate that it can count both subgraphs and induced subgraphs on random synthetic graphs while also achieving competitive performances on molecular datasets. |
| Researcher Affiliation | Academia | Zhengdao Chen New York University zc1216@nyu.edu Lei Chen New York University lc3909@nyu.edu Soledad Villar Johns Hopkins University soledad.villar@jhu.edu Joan Bruna New York University bruna@cims.nyu.edu |
| Pseudocode | No | The paper describes the models and methods using mathematical equations and textual explanations but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | 1Code available at https://github.com/leichen2018/GNN-Substructure-Counting. |
| Open Datasets | Yes | We evaluate LRP on the molecular prediction datasets ogbg-molhiv [63], QM9 [54] and ZINC [14]. |
| Dataset Splits | Yes | For the counting tasks, we generate a dataset of 1000 graphs, consisting of 800 training graphs, 100 validation graphs, and 100 testing graphs, all of them of size ranging from 10 to 50 nodes. For ogbg-molhiv, QM9 and ZINC, we follow the settings described in [63, 39, 14] for data splits... |
| Hardware Specification | Yes | Each model is trained on 1080ti five times with different random seeds. |
| Software Dependencies | No | The paper mentions various GNN models and general software components but does not provide specific version numbers for any libraries, frameworks, or solvers (e.g., Python, PyTorch, TensorFlow, CUDA versions). |
| Experiment Setup | Yes | We use Adam optimizer [30] with a learning rate of 0.001 and a weight decay of 10−5... For ogbg-molhiv, the total number of epochs is set to 1000 with a batch size of 32. For QM9, the total number of epochs is set to 2000 with a batch size of 128. For ZINC, the total number of epochs is set to 2000 with a batch size of 128. |