AKBR: Learning Adaptive Kernel-based Representations for Graph Classification
Authors: Lu Bai, Feifei Qian, Lixin Cui, Ming Li, Hangyuan Du, Yue Wang, Edwin Hancock
IJCAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we evaluate the performance of the proposed AKBR model against state-of-the-art graph kernels and deep learning methods. We use ten standard graph datasets extracted from bioinformatics (Bio), social networks (SN), and computer vision (CV). The OGBG-MOLBACE and OGBG-MOLBBBP datasets are selected from Open Graph Benchmark [Hu et al., 2020]. The Shock dataset can be obtained from [Siddiqi et al., 1999]. Other datasets from bioinformatics and social networks can be directly downloaded from [Morris et al., 2020]. We provide the graph number and the average graph size of each dataset in Table 1. Experimental results show that the proposed AKBR model outperforms existing state-of-the-art graph kernels and deep learning methods on standard graph benchmarks. |
| Researcher Affiliation | Academia | 1School of Artificial Intelligence, Beijing Normal University, Beijing, China; 2School of Information, Central University of Finance and Economics, Beijing, China; 3Zhejiang Institute of Optoelectronics, Jinhua, China; 4Zhejiang Key Laboratory of Intelligent Education Technology and Application, Zhejiang Normal University, Jinhua, China; 5School of Computer and Information Technology, Shanxi University, Taiyuan, China; 6Department of Computer Science, University of York, York, United Kingdom. EMAIL, feifei EMAIL, EMAIL |
| Pseudocode | No | The paper describes the proposed AKBR model's framework and definition using prose, mathematical equations, and figures, but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is publicly available1. 1https://github.com/Sophia0830BNU/AKBR |
| Open Datasets | Yes | We use ten standard graph datasets extracted from bioinformatics (Bio), social networks (SN), and computer vision (CV). The OGBG-MOLBACE and OGBG-MOLBBBP datasets are selected from Open Graph Benchmark [Hu et al., 2020]. The Shock dataset can be obtained from [Siddiqi et al., 1999]. Other datasets from bioinformatics and social networks can be directly downloaded from [Morris et al., 2020]. |
| Dataset Splits | Yes | We perform a 10-fold cross-validation and repeat the experiments ten times, and the average accuracy is reported in Table 2. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments, such as GPU or CPU models, or memory specifications. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies or libraries used in the implementation or experimentation. |
| Experiment Setup | Yes | Each model has been trained for 500 epochs on the first fold. We perform a 10-fold cross-validation and repeat the experiments ten times, and the average accuracy is reported in Table 2. |