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..
A Simple yet Effective Method for Graph Classification
Authors: Junran Wu, Shangzhe Li, Jianhao Li, Yicheng Pan, Ke Xu
IJCAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically validate our methods with several graph classification benchmarks and demonstrate that they achieve better performance and lower computational consumption than competing approaches. |
| Researcher Affiliation | Academia | 1State Key Lab of Software Development Environment, Beihang University, Beijing, 100191, China 2School of Mathematical Science, Beihang University, Beijing 100191, China |
| Pseudocode | Yes | Algorithm 1 k-dimensional coding tree on structural entropy |
| Open Source Code | Yes | The code of the WL-CT kernel and HRN can be found at https: //github.com/Wu-Junran/Hierarchical Reporting. |
| Open Datasets | Yes | Datasets. We conduct graph classification on five benchmarks: three social network datasets (IMDB-BINARY, IMDB-MULTI, and COLLAB) and two bioinformatics datasets (MUTAG and PTC) [Morris et al., 2020] |
| Dataset Splits | Yes | Configurations. Following [Xu et al., 2019], 10-fold crossvalidation is conducted, and we present the average accuracies achieved to validate the performance of our methods in graph classification |
| Hardware Specification | No | The paper does not provide specific details about the hardware used, such as GPU or CPU models. It only mentions computational efficiency in terms of FLOPs. |
| Software Dependencies | No | The paper mentions software like C-support vector machine (C-SVM), Scikit-learn, and Adam optimizer, but does not specify their version numbers. |
| Experiment Setup | Yes | Regarding the configuration of our tree kernel, we adopt the C-support vector machine (C-SVM) [Chang and Lin, 2011] as the classifier and tune the hyperparameter C of the SVM and the height of the coding tree [2, 3, 4, 5]. ... For configuration of HRN, the number of HRN iterations is consistent with the height of the associated coding trees, which is also [2, 3, 4, 5]. All MLPs have 2 layers... We utilize the Adam optimizer and set its initial learning rate to 0.01. For a better fit, the learning rate decays by half every 50 epochs. Other tuned hyperparameters for HRN include the number of hidden dimensions {16, 32, 64}, the minibatch size {32, 128}, and the dropout ratio {0, 0.5} after LAYERPOOL. |