A Simple yet Effective Method for Graph Classification
Authors: Junran Wu, Shangzhe Li, Jianhao Li, Yicheng Pan, Ke Xu
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | 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. |