Lovász Principle for Unsupervised Graph Representation Learning
Authors: Ziheng Sun, Chris Ding, Jicong Fan
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experiments demonstrate that our Lovász principles achieve competitive performance compared to the baselines in unsupervised and semi-supervised graph-level representation learning tasks. |
| Researcher Affiliation | Academia | Ziheng Sun1,2 Chris Ding1 Jicong Fan 1,2 1School of Data Science, The Chinese University of Hong Kong, Shenzhen, China 2Shenzhen Research Institute of Big Data, Shenzhen, China zihengsun@link.cuhk.edu.cn {chrisding,fanjicong}@cuhk.edu.cn |
| Pseudocode | Yes | In Algorithm 1, we propose a constrained optimization for the 'strict Lovász principle' via projection. Algorithm 1: Constrained optimization for 'strict Lovász principle'. Algorithm 2: The definition of projection function Proj U. |
| Open Source Code | Yes | The code of our Lovász principles is publicly available on Git Hub. Corresponding author https://github.com/Sun Ziheng0/Lovasz-Principle |
| Open Datasets | Yes | We conduct the experiments on TUD benchmark datasets [Morris et al., 2020] and Ch EMBL benchmark datasets [Mayr et al., 2018; Gaulton et al., 2012]. |
| Dataset Splits | Yes | We perform 10-fold cross-validation on each dataset and repeat 10 times with different random seeds and record the average accuracy (ACC) and standard deviation. For each fold, we use 80% of the total data as the unlabeled data, 10% as labeled training data, and 10% as labeled testing data. |
| Hardware Specification | Yes | We run the programming on a machine with Intel 7 CPU and RTX 3090 GPU. |
| Software Dependencies | No | The paper mentions using a '5-layer GIN [Xu et al., 2018]' and 'an SVM as the classifier' along with 'Res GCN [Chen et al., 2019]', but it does not specify version numbers for any of the software components or libraries, which are necessary for full reproducibility. |
| Experiment Setup | Yes | Specifically, we use a 5-layer GIN [Xu et al., 2018] with hidden size 128 as the representation model and an SVM as the classifier. The model is trained with a batch size of 128 and a learning rate of 0.001. For those contrastive learning methods (e.g., JOJOv2 and Auto GCL), we use 30 epochs of contrastive pre-training under the naive strategy. |