Contrastive Laplacian Eigenmaps
Authors: Hao Zhu, Ke Sun, Peter Koniusz
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate COLES on transductive and inductive node classification tasks. Node clustering is also evaluated. COLES is compared to state-of-the-art unsupervised, contrastive and (semi-)supervised methods. |
| Researcher Affiliation | Collaboration | Hao Zhu , Ke Sun , Piotr Koniusz *, , Data61/CSIRO Australian National University allenhaozhu@gmail.com, sunk@ieee.org, piotr.koniusz@data61.csiro.au |
| Pseudocode | No | The paper does not contain explicitly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | Yes | Code: https://github.com/allenhaozhu/COLES. |
| Open Datasets | Yes | COLES is evaluated on four citation networks: Cora, Citeseer, Pubmed, Cora Full [22, 6] for transductive setting. We also employ the large scale Ogbn-arxiv from OGB [19]. Finally, the Reddit [53] dataset is used in inductive setting. |
| Dataset Splits | Yes | Fixed data splits [51] for transductive tasks are often used in evaluations between different models. However, such an experimental setup may benefit easily overfitting models [38]. Thus, instead of fixed data splits, results are averaged over 50 random splits for each dataset and standard deviations are reported for empirical evaluation on transductive tasks. Moreover, we also test the performance under a different number of samples per class i.e., 5 and 20 samples per class. |
| Hardware Specification | Yes | For graphs with more than 100 thousands nodes and 10 millions edges (Reddit), our model runs smoothly on NVIDIA 1080 GPU. |
| Software Dependencies | No | The paper mentions software components like 'Adam optimizer' and 'Geo Torch' but does not specify their version numbers or other software dependencies with versions required for reproducibility. |
| Experiment Setup | Yes | In the transductive experiments, the detailed hyperparameter settings for Cora, Citeseer, Pubmed, and Cora Full are listed below. For COLES, we use the Adam optimizer with learning rates of [0.001, 0.0001, 0.02, 0.02] and the decay of [5e 4, 1e 3, 5e 4, 2e 4]. The number of training epochs are [20, 20, 100, 30], respectively. We sample 10 randomized adjacent matrices, and 5 negative samples for each node in each matrix on each dataset before training. For the S2GC and COLES-S2GC, the number of propagation steps (layers) are 8 for all datasets except Cora Full (2 steps). For SGC and COLES-SGC, we use 2 steps for all datasets. |