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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
CuCo: Graph Representation with Curriculum Contrastive Learning
Authors: Guanyi Chu, Xiao Wang, Chuan Shi, Xunqiang Jiang
IJCAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on fifteen graph classification real-world datasets, as well as the parameter analysis, well demonstrate that our proposed Cu Co yields truly encouraging results in terms of performance on classification and convergence. |
| Researcher Affiliation | Academia | Guanyi Chu , Xiao Wang , Chuan Shi and Xunqiang Jiang Beijing University of Posts and Telecommunications EMAIL |
| Pseudocode | Yes | Algorithm 1 Training procedure of Cu Co |
| Open Source Code | No | The paper does not provide any explicit statement about open-sourcing its code, nor does it include a link to a code repository. |
| Open Datasets | Yes | We evaluate model performance on seven classical graph classification benchmarks shown in Table 1... we evaluate model performance on eight Open Graph Benchmark (OGB) [Weihua et al., 2020b] molecule property prediction datasets. |
| Dataset Splits | Yes | We use 10-fold cross validation accuracy to report the classification performance. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory specifications) used for running the experiments. |
| Software Dependencies | No | The paper mentions employing 'graph neural networks (GNNs)' and specifically 'a three-layer Graph Isomorphism Network (GIN)', but it does not specify software dependencies with version numbers (e.g., PyTorch 1.x, TensorFlow 2.x, Python 3.x). |
| Experiment Setup | Yes | For our proposed model, we adopt a three-layer Graph Isomorphism Network (GIN) with 32-dimensional hidden units and a sum pooling readout function for performance comparisons. We use 10-fold cross validation accuracy to report the classification performance. Experiments are repeated 5 times. |