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..
Generalized Laplacian Eigenmaps
Authors: Hao Zhu, Piotr Koniusz
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show on popular benchmarks/backbones that GLEN offers favourable accuracy/scalability compared to state-of-the-art baselines. 6 Experiments We evaluate GLEN (its relaxation) on transductive and inductive node classification tasks and node clustering. GLEN is compared to popular unsupervised, contrastive, and (semi-)supervised approaches. Except for the classifier, unsupervised models do not use labels. To train a graph encoder in an unsupervised manner, GCN [17] minimizes a reconstruction error which only considers the similarity matrix and ignores the dissimilarity information. |
| Researcher Affiliation | Collaboration | Hao Zhu , Piotr Koniusz *, , Data61/CSIRO Australian National University EMAIL, EMAIL |
| Pseudocode | No | No pseudocode or clearly labeled algorithm block was found in the paper. |
| Open Source Code | Yes | *The corresponding author. Code: https://github.com/allenhaozhu/GLEN. |
| Open Datasets | Yes | Datasets. GLEN is evaluated on four citation networks: Cora, Citeseer, Pubmed, Cora Full [17, 4] for transductive setting. We also employ the large scale Ogbn-arxiv from OGB [14]. |
| Dataset Splits | Yes | Metrics. As fixed data splits [45] often on transductive models benefit models that overfit, we average results over 50 random splits for each dataset. We evaluate performance for 5 and 20 samples per class. Nonetheless, we also evaluate our model on the standard splits. |
| Hardware Specification | Yes | For graphs with over 100 thousands nodes and 10 millions edges (Reddit), GLEN runs fast on NVIDIA 1080 GPU. |
| Software Dependencies | No | The paper mentions software used (e.g., GCN, SGC, S2GC) but does not provide specific version numbers for any software components or libraries (e.g., Python version, PyTorch version, etc.). |
| Experiment Setup | No | We set hyperparameters based on the settings described in prior papers. (Appendix E for implementation details, but not specified in the main text.) |