Decoupled Self-supervised Learning for Graphs
Authors: Teng Xiao, Zhengyu Chen, Zhimeng Guo, Zeyang Zhuang, Suhang Wang
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on various types of graph benchmarks demonstrate that our proposed framework can achieve better performance compared with competitive baselines. |
| Researcher Affiliation | Academia | 1The Pennsylvania State University, 2Zhejiang University, 3Tongji University |
| Pseudocode | Yes | Our full algorithm and network are provided in Appendix B. Algorithm 1: Decoupled Self-supervised Learning (DSSL) Algorithm |
| Open Source Code | No | The paper self-assesses 'Yes' to including code, data, and instructions in supplemental material or as a URL, but no explicit statement or link confirming open-source code availability for the methodology described in the paper was found within the main paper content or its appendices. |
| Open Datasets | Yes | We perform experiments on widely-used homophilic graph datasets: Cora, Citeseer, and Pubmed [42], as well as non-homophilic datasets: Texas, Cornell, Wisconsin [37], Penn94 and Twitch. |
| Dataset Splits | Yes | For datasets, we adopt the similar random split with a train/validation/test split ratio of 48/32/20% for the training of downstream linear classifier following [37]. |
| Hardware Specification | No | No specific hardware details (such as GPU/CPU models, memory, or detailed computer specifications) used for running the experiments were provided in the paper. |
| Software Dependencies | No | The paper mentions software components like GCN, Adam, Gumbel-Softmax estimator, and K-means, but does not provide specific version numbers for any software dependencies or libraries. |
| Experiment Setup | Yes | We select the best configuration of hyper-parameters based on accuracy on the validation. The detailed settings are given in Appendix E.2. |