Graph As Point Set
Authors: Xiyuan Wang, Pan Li, Muhan Zhang
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In our experiments, we evaluate our model across three dimensions: substructure counting for short-range expressivity, real-world graph property prediction for practical performance, and Long-Range Graph Benchmarks (Dwivedi et al., 2022b) to assess long-range interactions. |
| Researcher Affiliation | Academia | 1Institute for Artificial Intelligence, Peking University 2Georgia Institute of Technology. |
| Pseudocode | No | The paper describes its methods in text and uses figures to illustrate architectures (e.g., Figure 1, Figure 3, Figure 4), but it does not include any explicitly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | Yes | Our code is available at https://github.com/GraphPKU/GraphAsSet. |
| Open Datasets | Yes | Synthetic is the dataset used in substructure counting tasks provided by Huang et al. (2023b), they are random graph with the count of substructure as node label. QM9 (Wu et al., 2017), ZINC (G omez Bombarelli et al., 2016), and ogbg-molhiv are three datasets of molecules. |
| Dataset Splits | Yes | In split column, fixed means the dataset uses the split provided in the original release. Otherwise, it is of the formal training set ratio/valid ratio/test ratio. ... Synthetic 0.3/0.2/0.5. QM9 0.8/0.1/0.1 ... MUTAG ... 10-fold cross validataion |
| Hardware Specification | Yes | All our experiments are conducted on NVIDIA RTX 3090 GPUs on a linux server. |
| Software Dependencies | No | Our code is primarily based on Py Torch (Paszke et al., 2019) and Py G (Fey & Lenssen, 2019). (Specific version numbers for PyTorch and PyG are not provided.) |
| Experiment Setup | Yes | We use l1 loss for regression tasks and cross entropy loss for classification tasks... For optimization, we use Adam W optimizer and cosine annealing scheduler. Hyperparameters for datasets are shown in Table 7. |