EIGNN: Efficient Infinite-Depth Graph Neural Networks
Authors: Juncheng Liu, Kenji Kawaguchi, Bryan Hooi, Yiwei Wang, Xiaokui Xiao
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The empirical results of comprehensive experiments on synthetic and real-world datasets show that EIGNN has a better ability to capture long-range dependencies than recent baselines, and consistently achieves state-of-the-art performance. In this section, we demonstrate that EIGNN can effectively learn representations which have the ability to capture long-range dependencies in graphs. Therefore, EIGNN achieves state-of-the-art performance for node classification task on both synthetic and real-world datasets. Specifically, we conduct experiments1 to compare EIGNN with representative baselines on seven graph datasets (Chains, Chameleon, Squirrel, Cornell, Texas, Wisconsin, and PPI) |
| Researcher Affiliation | Academia | National University of Singapore {juncheng,kenji,bhooi}@comp.nus.edu.sg wangyw_seu@foxmail.com, xkxiao@nus.edu.sg |
| Pseudocode | No | No explicit pseudocode or algorithm blocks found. |
| Open Source Code | Yes | 1The implementation can be found at https://github.com/liu-jc/EIGNN |
| Open Datasets | Yes | Specifically, we conduct experiments1 to compare EIGNN with representative baselines on seven graph datasets (Chains, Chameleon, Squirrel, Cornell, Texas, Wisconsin, and PPI), where Chains is a synthetic dataset used in Gu et al. [10]. Chameleon, Squirrel, Cornell, Texas, and Wisconsin are real-world datasets with a single graph each [21] while PPI is a real-world dataset with multiple graphs [11]. Detailed descriptions of datasets and settings about experiments can be found in Appendix C. |
| Dataset Splits | Yes | For training/validation/testing split, we consider 5%/10%/85% which is similar with the semi-supervised node classification setting [14]. |
| Hardware Specification | No | The paper mentions training times but does not provide specific hardware details such as CPU/GPU models or memory specifications. |
| Software Dependencies | No | The paper mentions PyTorch [20] but does not provide specific version numbers for software dependencies or libraries. |
| Experiment Setup | Yes | The hyper-parameter setting and details about baselines implementation can be found in Appendix C.2. |