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..
LazyGNN: Large-Scale Graph Neural Networks via Lazy Propagation
Authors: Rui Xue, Haoyu Han, Mohamadali Torkamani, Jian Pei, Xiaorui Liu
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experiments demonstrate its superior prediction performance and scalability on large-scale benchmarks. The implementation of Lazy GNN is available at https: //github.com/RXPHD/Lazy_GNN. |
| Researcher Affiliation | Collaboration | 1North Carolina State University, Raleigh, US 2Michigan State University, East Lansing, US 3Amazon, US (this work does not relate to the author s position at Amazon) 4Duke University, Durham, US. |
| Pseudocode | No | The paper includes conceptual diagrams (Figure 3 and Figure 4) but no structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The implementation of Lazy GNN is available at https: //github.com/RXPHD/Lazy_GNN. |
| Open Datasets | Yes | We conduct experiments on multiple large-scale graph datasets including REDDIT, YELP, FLICKR, ogbn-arxiv, and ogbn-products (Hu et al., 2020). |
| Dataset Splits | Yes | We conduct experiments on multiple large-scale graph datasets including REDDIT, YELP, FLICKR, ogbn-arxiv, and ogbn-products (Hu et al., 2020). The hyperparameter tuning of baselines closely follows the setting in GNNAuto Scale (Fey et al., 2021). The convergence of validation accuracy in Figure 5 demonstrates that Lazy GNN has a comparable convergence speed with GCN (GAS) and GCNII (GAS), and is slightly faster than APPNP (GAS) in terms of the number of training epochs. |
| Hardware Specification | No | The paper mentions running experiments on CPU and GPU memory but does not specify particular models, types, or configurations of the hardware used. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies or libraries used. |
| Experiment Setup | Yes | For Lazy GNN, hyperparameters are tuned from the following search space: (1) learning rate: {0.01, 0.001, 0.0001}; (2) weight decay: {0, 5e 4, 5e 5}; (3) dropout: {0.1, 0.3, 0.5, 0.7}; (4) propagation layers : L {1, 2}; (5) MLP layers: {3, 4}; (6) MLP hidden units: {256, 512}; (7) α {0.01, 0.1, 0.2, 0.5, 0.8}; (8) β and γ are simply set as 0.5 in most cases, but a further tuning can improve the performance. |