Eliciting Structural and Semantic Global Knowledge in Unsupervised Graph Contrastive Learning
Authors: Kaize Ding, Yancheng Wang, Yingzhen Yang, Huan Liu
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments demonstrate that the node representations learned by S3-CL achieve superior performance on different downstream tasks compared with the state-of-the-art unsupervised GCL methods. |
| Researcher Affiliation | Academia | Arizona State University School of Computing and Augmented Intelligence kaize.ding@asu.edu, yancheng.wang@asu.edu, yingzhen.yang@asu.edu, huan.liu@asu.edu |
| Pseudocode | Yes | Algorithm 1 outlines the learning process of the proposed framework. |
| Open Source Code | Yes | Implementation and more experimental details are publicly available at https://github.com/kaize0409/S-3-CL. |
| Open Datasets | Yes | In our experiments, we evaluate S3CL on six public benchmark datasets that are widely used for node representation learning, including Cora (Sen et al. 2008), Citeseer (Sen et al. 2008), Pubmed (Namata et al. 2012), Amazon-P (Shchur et al. 2018), Coauthor CS (Shchur et al. 2018) and ogbn-arxiv (Hu et al. 2020). |
| Dataset Splits | Yes | We follow the evaluation protocols in previous works (Veliˇckovi c et al. 2019; Hu et al. 2020) for node classification. |
| Hardware Specification | No | No specific hardware details such as GPU/CPU models, memory, or processor types used for running experiments are provided in the paper. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers, such as Python or library versions. |
| Experiment Setup | Yes | To demonstrate the power of our approach in utilizing structural global knowledge, we compare S3-CL against GRACE, MVGRL, MERIT, and SUGRL with different numbers of layers L. The node clustering accuracy of different methods is shown in Figure 3. ... To validate the effectiveness of the structural contrastive learning and semantic contrastive learning in S3CL, we conduct an ablation study on Citesser, Cora, and Pubmed with two variants of S3-CL, each of which has one of the contrastive learning components removed. |