Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning
Authors: Ming Jin, Yizhen Zheng, Yuan-Fang Li, Chen Gong, Chuan Zhou, Shirui Pan
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To demonstrate the effectiveness of our approach, we perform empirical experiments on five real-world datasets. |
| Researcher Affiliation | Academia | 1Department of Data Science and AI, Faculty of IT, Monash University, Australia 2School of Computer Science and Engineering, Nanjing University of Science and Technology, China 3Academy of Mathematics and Systems Science, Chinese Academy of Sciences, China |
| Pseudocode | No | The paper describes the proposed method in prose and through diagrams, but does not include a formal pseudocode or algorithm block. |
| Open Source Code | Yes | Code is made available at https://github.com/GRAND-Lab/MERIT |
| Open Datasets | Yes | To evaluate the effectiveness of MERIT on self-supervised node representation learning, we conduct extensive experiments on five widely used benchmark datasets, including Cora, Cite Seer, Pub Med, Amazon Photo [Shchur et al., 2018], and Coauthor CS [Shchur et al., 2018]. The dataset statistics are summarized in Table 1. |
| Dataset Splits | Yes | For Cora, Citeseer and Pub Med, we follow the same data splits as in [Yang et al., 2016]. For Amazon Photo and Coauthor CS, we include 30 randomly-selected nodes per class to construct the training and validation set, while using the remaining nodes as the testing set. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper mentions using GCN as a backbone encoder but does not provide specific version numbers for software dependencies or libraries. |
| Experiment Setup | Yes | For model tuning, we perform the grid search on primary hyperparameters over certain ranges. The latent dimension of graph encoders, projectors, and the predictor is fixed to 512. We tune momentum m and augmentation ratio P between 0 and 1. To balance the effect of two contrastive schemes, we tune β within {0.2, 0.4, 0.6, 0.8}. To evaluate the trained graph encoder, we adopt a linear evaluation protocol by training a separate logistic regression classifier on top of the learned node representations. |