InfoGCL: Information-Aware Graph Contrastive Learning
Authors: Dongkuan Xu, Wei Cheng, Dongsheng Luo, Haifeng Chen, Xiang Zhang
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically validate our theoretical analysis on both node and graph classification benchmark datasets, and demonstrate that our algorithm significantly outperforms the state-of-the-arts. |
| Researcher Affiliation | Collaboration | 1The Pennsylvania State University 2NEC Labs America 1{dux19,dul262,xzz89}@psu.edu 2{weicheng,haifeng}@nec-labs.com |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described in this paper. |
| Open Datasets | Yes | We use both graph classification and node classification benchmark datasets that are widely used in the existing graph contrastive learning approaches. The graph classification datasets include MUTAG [17], PTC-MR [17], IMDB-B [40], IMDB-M [40], NCI1 [34], and COLLAB [40]. The node classification datasets include Citeseer, Cora, and Pubmed [23]. |
| Dataset Splits | Yes | For graph classification, we report the mean 10-fold cross validation accuracy after 5 runs followed by a linear SVM. The linear SVM is trained by applying cross validation on training data folds and the best mean accuracy is reported. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | We conduct experiment with the values of the number of GNN layers, the number of epochs, batch size, the parameter C of SVM in the sets {2, 4, 8, 12}, {10, 20, 40, 100}, {32, 64, 128, 256} and {10 3, 10 2, ..., 102, 103 }, respectively. We adopt the basic setting of DGI for node classification. Specifically, we set the number of GNN layers to 1 and experiment with the batch size in the set {2, 4, 8}. The hidden dimension of representations is set to 512. We also apply the early stopping strategy. |