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..
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 | 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. |