Flexible Infinite-Width Graph Convolutional Neural Networks
Authors: Ben Anson, Edward Milsom, Laurence Aitchison
TMLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We test the effect of representation learning empirically, training graph convolutional DKMs on several node classification benchmark datasets that exhibit varied levels of homophily. The dataset statistics can be found in Table 3. We trained with different regularization strengths, ν {0, 10 2, 10 1, 100, 101, 102, 103} to control the amount of representation learning. |
| Researcher Affiliation | Academia | Ben Anson EMAIL School of Mathematics University of Bristol Bristol, United Kingdom Edward Milsom EMAIL School of Mathematics University of Bristol Bristol, United Kingdom Laurence Aitchison EMAIL School of Computer Science University of Bristol Bristol, United Kingdom |
| Pseudocode | Yes | Algorithm 1 Graph convolutional DKM node classification |
| Open Source Code | No | The paper mentions that "The models were written in Pytorch" and that code was "adapted ... from Niu et al. (2023)" for certain datasets, but it does not provide any explicit statement about releasing its own source code or a link to a code repository for the methodology described. |
| Open Datasets | Yes | For node classification datasets, we used train/validation/test splits from the torch_geometric library (Fey & Lenssen, 2019) for most datasets; the exceptions were Arxiv and Reddit for which we adapted code from Niu et al. (2023). |
| Dataset Splits | Yes | For node classification datasets, we used train/validation/test splits from the torch_geometric library (Fey & Lenssen, 2019) for most datasets; the exceptions were Arxiv and Reddit for which we adapted code from Niu et al. (2023). ... For graph classification datasets, we constructed our own cross-validation splits (10 splits) |
| Hardware Specification | Yes | The models were written in Pytorch, and we trained on a cluster containing RTX 2080 s, RTX 3090 s and A100s. |
| Software Dependencies | No | The paper mentions using Pytorch, the torch_geometric library, and the Adam optimizer, but does not provide specific version numbers for any of these software dependencies. |
| Experiment Setup | Yes | We used a 2-layer architecture, with the adjacency renormalization described by Kipf & Welling (2017), and no residual connections or normalization layers. ... We trained for 300/200 epochs in sweep (1), and in the remaining sweeps we trained for 200/150 for the smaller/bigger datasets... We used the Adam optimizer with a two-stage learning rate schedule for all training runs. We increase the learning rate linearly from 10 3 to 10 2 for the first quarter of the epochs, and after that use a cosine schedule with a minimum learning rate of 10 5. |