Relational Pooling for Graph Representations
Authors: Ryan Murphy, Balasubramaniam Srinivasan, Vinayak Rao, Bruno Ribeiro
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate improved performance of RP-based graph representations over state-of-the-art methods on a number of tasks. In our experiments, we classify graphs that cannot be distinguished by a state-of-the-art WL-GNN (Xu et al., 2019). We demonstrate empirically that these still lead to strong performance and can be used with RP-GNN to speed up graph classification when compared to traditional WL-GNNs. |
| Researcher Affiliation | Academia | 1Department of Statistics, and 2Department of Computer Science, Purdue University, West Lafayette, Indiana, USA. Correspondence to: Ryan L. Murphy <murph213@purdue.edu>. |
| Pseudocode | No | The paper describes algorithms and methods using mathematical notation and textual descriptions, but it does not provide any formal pseudocode blocks or sections explicitly labeled "Algorithm". |
| Open Source Code | Yes | Our code is on Git Hub2. 2https://github.com/PurdueMINDS/RelationalPooling |
| Open Datasets | Yes | We chose datasets from the Molecule Net project (Wu et al., 2018) which collects chemical datasets and collates the performance of various models that yield classification tasks and on which graph-based methods achieved superior performance3. In particular, we chose MUV (Rohrer & Baumann, 2009), HIV, and Tox21 (Mayr et al., 2016; Huang et al., 2016). |
| Dataset Splits | Yes | We evaluate GIN and RP-GIN with five-fold cross validation with balanced classes on both training and validation on this task. ... We train over 20 random data splits. ... evaluate using five random train/val/test splits. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments (e.g., CPU, GPU models, or memory specifications). |
| Software Dependencies | No | The paper mentions using "Deep Chem" but does not specify a version number or list any other software dependencies with their versions, which is necessary for reproducibility. |
| Experiment Setup | No | Further implementation and training details are in the Supplementary Material. Model architectures, hyperparameters, and training procedures are detailed in the Supplementary Material. |