Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
EIGNN: Efficient Infinite-Depth Graph Neural Networks
Authors: Juncheng Liu, Kenji Kawaguchi, Bryan Hooi, Yiwei Wang, Xiaokui Xiao
NeurIPS 2021 | Venue PDF | 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 EMAIL EMAIL, EMAIL |
| 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. |