PlanE: Representation Learning over Planar Graphs
Authors: Radoslav Dimitrov, Zeyang Zhao, Ralph Abboud, Ismail Ceylan
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct an empirical analysis for BASEPLANE evaluating its expressive power on three tasks (Section 7.1), and validating its performance on real-world graph classification (Section 7.2) and regression benchmarks (Section 7.3 and 7.4). |
| Researcher Affiliation | Academia | Radoslav Dimitrov Department of Computer Science University of Oxford contact@radoslav11.com Zeyang Zhao Department of Computer Science University of Oxford zeyzang.zhao@cs.ox.ac.uk Ralph Abboud Department of Computer Science University of Oxford ralph@ralphabb.ai Ismail Ilkan Ceylan Department of Computer Science University of Oxford ismail.ceylan@cs.ox.ac.uk This work is largely conducted while these authors were still affiliated with the University of Oxford. |
| Pseudocode | No | The paper describes algorithms using mathematical equations and descriptive text, but does not include any explicitly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | Yes | The code for our experiments, as well as instructions to reproduce our results and set up dependencies, can be found at this Git Hub repository: https://github.com/ZZYSonny/Plan E |
| Open Datasets | Yes | We evaluate BASEPLANE on the planar EXP benchmark [1]...Mol HIV graph classification task from OGB [33]...We evaluate E-BASEPLANE on all 13 QM9 properties...Graph regression on ZINC. (Citations for EXP, OGB, QM9, ZINC indicate public availability). Data. We select a subset QM9CC of graphs from QM9 to obtain a diverse distribution of CCs. |
| Dataset Splits | Yes | We apply the earlier filtering on the original QM9 splits to obtain train/validation/test sets that are direct subsets of the full QM9 splits, and which consist of 44226, 3941 and 3921 graphs, respectively. |
| Hardware Specification | Yes | We run all experiments on 4 cores from Intel Xeon Platinum 8268 CPU @ 2.90GHz with 32GB RAM. |
| Software Dependencies | No | The paper mentions using the Adam optimizer [39] but does not provide specific software dependencies with version numbers (e.g., Python, PyTorch, CUDA versions). |
| Experiment Setup | Yes | All experimental details, including hyperparameters, can be found in Appendix D.4. For example, for Graph classification on EXP: we use a 2-layer BASEPLANE model with 64-dimensional node embeddings. We instantiate the triconnected component encoder with 16-dimensional positional encodings, each computed using a periodicity of 64. ...train BASEPLANE on each fold for 50 epochs using the Adam [39] optimizer with a learning rate of 10^-3, and binary cross entropy loss. |