Weisfeiler and Lehman Go Paths: Learning Topological Features via Path Complexes
Authors: Quang Truong, Peter Chin
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical validation of our assertions is offered through evaluations of PCN on various real-world benchmarks and a collection of 9 strongly regular graph (SRGs) families. Notably, our proposed approach achieves superior performance without the need for assumptions about graph sub-structures other than paths, which are inherent in every connected graph. |
| Researcher Affiliation | Academia | Thayer School of Engineering, Dartmouth College cong.minh.quang.truong.th@dartmouth.edu, peter.chin@dartmouth.edu |
| Pseudocode | No | The paper describes the PWL procedure and message-passing updates in narrative text and equations, but does not provide a formally structured pseudocode or algorithm block. |
| Open Source Code | No | Please refer to the Appendix of the extended version for the detailed proofs, formulae, experiments, computational analysis, and other resources. https://arxiv.org/abs/2308.06838 |
| Open Datasets | Yes | TUDataset benchmarks, encompassing a broad spectrum of graph datasets from biology, chemistry, and social networks, are proposed in (Morris et al. 2020). We also evaluate our model on the ZINC dataset, a common graph benchmark for regression tasks (Sterling and Irwin 2015; Dwivedi et al. 2023). We also evaluate our model on the OGBG-MOLHIV dataset (Hu et al. 2020), which contains about 41k graphs for the graph binary classification task. |
| Dataset Splits | Yes | Specifically, we report the highest mean test accuracy across a 10-fold cross-validation as indicated in (Xu et al. 2019). Hu et al. applies a scaffold splitting procedure (Wu et al. 2018) for the OGBG-MOLHIV dataset; thus we report the performance of our model on both the validation and test sets in Table 2. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware used (e.g., GPU/CPU models, memory amounts) for running its experiments. |
| Software Dependencies | No | The paper mentions several software tools and libraries (e.g., PyTorch Geometric, Weights and Biases, PyTorch, NetworkX, graph-tool), but does not provide specific version numbers for them. |
| Experiment Setup | No | Detailed hyperparameter settings along with relevant ablation studies are documented in the Appendix. These details are not present in the provided main text of the paper. |