Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Rethinking Dimensional Rationale in Graph Contrastive Learning from Causal Perspective
Authors: Qirui Ji, Jiangmeng Li, Jie Hu, Rui Wang, Changwen Zheng, Fanjiang Xu
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The conducted exploratory experiments attest to the feasibility of the aforementioned roadmap. Empirically, compared with state-of-the-art methods, our method can yield significant performance boosts on various benchmarks with respect to discriminability and transferability. |
| Researcher Affiliation | Academia | 1Science & Technology on Integrated Information System Laboratory, Institute of Software Chinese Academy of Sciences 2State Key Laboratory of Intelligent Game 3University of Chinese Academy of Sciences 4State Key Laboratory of Computer Science, Institute of Software Chinese Academy of Sciences |
| Pseudocode | Yes | Algorithm 1: The DRGRL training algorithm |
| Open Source Code | Yes | The code implementation of our method is available at https://github.com/Byron Ji/DRGCL. |
| Open Datasets | Yes | For unsupervised learning, we benchmark DRGCL on eight established datasets in TU datasets (Morris et al. 2020). |
| Dataset Splits | Yes | mean 10-fold cross-validation accuracy with 5 runs. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU models, CPU types, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | The details of our model architectures and corresponding hyper-parameters are summarized in Table 6. Table 6 includes details such as Backbone neuron [32,32,32], Projection neuron [512,512,512], Pre-train lr 0.01, Finetune lr {0.01,0.001,0.0001}, Temperature τ 0.1, Traning epochs 20, Trade-off parameter λ 0.001, Trade-off parameter α 10. |