Self-Supervised Representation Learning via Latent Graph Prediction
Authors: Yaochen Xie, Zhao Xu, Shuiwang Ji
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental results demonstrate the superiority of La Graph in performance and the robustness to the decreasing training sample size on both graph-level and node-level tasks. |
| Researcher Affiliation | Academia | 1Department of Computer Science & Engineering, Texas A&M University, College Station, USA. Correspondence to: Yaochen Xie <ethanycx@tamu.edu>, Shuiwang Ji <sji@tamu.edu>. |
| Pseudocode | Yes | The pseudo-code for node-level and graph-level objective computations are provided in Algorithm 1 and Algorithm 2, respectively. |
| Open Source Code | Yes | Our code is available under the DIG library 1 (Liu et al., 2021a). 1https://github.com/divelab/DIG. |
| Open Datasets | Yes | For graph-level tasks, we follow Graph CL (You et al., 2020) to perform evaluations on eight graph classification datasets (Wale & Karypis, 2006; Borgwardt et al., 2005; Dobson & Doig, 2003; Debnath et al., 1991; Yanardag & Vishwanathan, 2015) from TUDataset (Morris et al., 2020). For node-level tasks, as the citation network datasets (Mc Callum et al., 2000; Giles et al., 1998; Sen et al., 2008) are recognized to be saturated and unreliable for GNN evaluation (Shchur et al., 2018; Thakoor et al., 2021), we follow Thakoor et al. (2021) to include four transductive node classification datasets from Shchur et al. (2018), including Amazon Computers, Amazon Photos from the Amazon Co-purchase Graph (Mc Auley et al., 2015), Coauthor CS, and Coauthor Physics from the Microsoft Academic Graph (Sinha et al., 2015). We further include three larger-scale inductive datasets, PPI, Reddit, and Flickr, for node-level classification used in SUBG-CON (Jiao et al., 2020). |
| Dataset Splits | Yes | To split train, valid and test sets, we use the public split used in (Shchur et al., 2018) for Coauthor and Amazon, (Zitnik & Leskovec, 2017; Hamilton et al., 2017; Zeng et al., 2020) for PPI, Reddit and Flickr provided by Py Torch Geometric. |
| Hardware Specification | Yes | We train graph-level datasets on a 11GB Ge Force RTX 2080 Ti GPU, and node-level datasets on a 56GB Nvidia RTX A6000 GPU. |
| Software Dependencies | Yes | Our experiments are implemented with Py Torch 1.7.0 and Py Torch Geometric 1.7.0. |
| Experiment Setup | Yes | Detailed model configurations, training settings, and dataset statistics are provided in Appendix C. ... Table 5. Model configurations for graph-level datasets. ... Table 6. Model configurartions for node-level datasets. |