Co-Modality Graph Contrastive Learning for Imbalanced Node Classification
Authors: Yiyue Qian, Chunhui Zhang, Yiming Zhang, Qianlong Wen, Yanfang Ye, Chuxu Zhang
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate that our model significantly outperforms state-of-the-art baseline models and learns more balanced representations on real-world graphs. |
| Researcher Affiliation | Academia | 1Department of Compute Science and Engineering, University of Notre Dame, USA 2Department of Computer Science, Brandeis University, USA 3Department of Computer and Data Sciences, Case Western Reserve University, USA |
| Pseudocode | Yes | Pseudo-code of CM-GCL is provided in Section A of the Appendix. |
| Open Source Code | Yes | Our source code is available at https://github.com/graphprojects/CM-GCL. |
| Open Datasets | Yes | In this paper, we adopt four multi-modality graph datasets from existing works, i.e., AMiner [36], Yelp Chi [33], Git Hub [29], and Instagram [30], which contain the raw content (e.g., text or image) and the graph structure information. |
| Dataset Splits | Yes | We use 70% samples for training, 10% for validation, and the remaining 20% for testing. |
| Hardware Specification | Yes | All experiments are conducted under the environment of the Ubuntu 16.04 OS, plus Intel i9-9900k CPU, two Ge Force GTX 2080 Ti Graphics Cards, and 64 GB of RAM. |
| Software Dependencies | No | The paper mentions 'Ubuntu 16.04 OS' but does not specify specific software libraries or solvers with version numbers (e.g., PyTorch, TensorFlow, scikit-learn versions). |
| Experiment Setup | Yes | With the grid search, pruning ratio e is set as 20%, the number of contrastive pairs R for each node in intra-modality GCL is different for different graphs (e.g., 5 for AMiner graph) , and the number of mini-batch n is different for different tasks, (e.g., 100 for AMiner graph). Besides, the temperature parameter τinter and τintra are set as 0.1 and the trade-off hyper-parameter λ among co-modality GCL is set as 0.5. For fine-tuning, α and γ in Lfocal for different graphs are different (e.g., (0.75, 1.0) for AMiner graph). |