Simple and Efficient Heterogeneous Graph Neural Network
Authors: Xiaocheng Yang, Mingyu Yan, Shirui Pan, Xiaochun Ye, Dongrui Fan
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on five real-world heterogeneous graphs demonstrate the superiority of Se HGNN over the state-of-the-arts on both accuracy and training speed. |
| Researcher Affiliation | Academia | 1 State Key Lab of Processors, Institute for Computing Technology, Chinese Academy of Sciences, China 2 School of Information and Communication Technology, Griffith University, Australia 3 School of Computer Science and Technology, University of Chinese Academy of Sciences, China {yangxiaocheng, yanmingyu}@ict.ac.cn, s.pan@griffith.edu.au, {yexiaochun, fandr}@ict.ac.cn |
| Pseudocode | Yes | Algorithm 1: The overall training process of Se HGNN |
| Open Source Code | Yes | Codes are available at https://github.com/ICT-GIMLab/Se HGNN. |
| Open Datasets | Yes | Experiments are conducted on four widely-used datasets from HGB benchmark (Lv et al. 2021), as well as a large-scale dataset ogbn-mag from OGB challenge (Hu et al. 2020a). |
| Dataset Splits | Yes | All results presented in this section are the average of 20 runs with different data partitions to mitigate the influence of random noise. [...] The ogbn-mag dataset presents two extra challenges: (1) some types of nodes lack raw features, (2) target-type nodes are split according to years, causing training nodes and test nodes to have different data distributions. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory amounts) used for running experiments were mentioned in the paper. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., Python 3.8, PyTorch 1.9, CUDA 11.1) were mentioned in the paper. |
| Experiment Setup | No | The details about all experiment settings and the network configurations are recorded in Appendix1. |