Representation Learning on Spatial Networks
Authors: Zheng Zhang, Liang Zhao
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the strength of our proposed framework through extensive experiments on both synthetic and real-world datasets. |
| Researcher Affiliation | Academia | Zheng Zhang Department of Computer Science Emory University Atlanta, GA 30322, USA zheng.zhang@emory.edu Liang Zhao Department of Computer Science Emory University Atlanta, GA 30322, USA liang.zhao@emory.edu |
| Pseudocode | No | The paper describes operations and model blocks but does not include a clearly labeled pseudocode or algorithm block. |
| Open Source Code | Yes | The code for the proposed model is available at https://github.com/rollingstonezz/SGMP_code. |
| Open Datasets | Yes | We experiment on 5 chemical molecule benchmark datasets from [94], including both classification (BACE, BBBP) and regression (ESOL, LIPO, QM9). Particularly, QM9 is a multi-task regression benchmark with 12 quantum mechanics properties. The data is obtained from the pytorch-geometric library [35]. and We also conducted an experiment using the structural connectivity (SC) of the brain network to predict the age of the subjects, which is a significant task in understanding the aging process of the human brain [50]. In specific, SC is processed from the Magnetic Resonance Imaging (MRI) data obtained from the human connectome project (HCP) [82]. |
| Dataset Splits | No | The paper states that training details including data splits were specified (in the checklist), but the main text does not explicitly provide specific percentages or sample counts for train/validation/test splits for the datasets used. |
| Hardware Specification | Yes | All experiments are conducted on a 64-bit machine with an NVIDIA GPU (GTX 1080 Ti, 11016 MHz, 11 GB GDDR5). |
| Software Dependencies | No | The proposed method is implemented with Pytorch deep learning framework [70]. |
| Experiment Setup | Yes | The experimental settings are introduced first, then the performance of the proposed method is presented through a set of comprehensive experiments. All experiments are conducted on a 64-bit machine with an NVIDIA GPU (GTX 1080 Ti, 11016 MHz, 11 GB GDDR5). The proposed method is implemented with Pytorch deep learning framework [70]. and We vary the size and other parameters (according to Appendix. C for details) of spatial networks to collect 3, 200 samples in our synthetic dataset. |