Generalized Laplacian Eigenmaps
Authors: Hao Zhu, Piotr Koniusz
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | 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 allenhaozhu@gmail.com, piotr.koniusz@data61.csiro.au |
| 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.) |