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..
Simple and Efficient Heterogeneous Graph Neural Network
Authors: Xiaocheng Yang, Mingyu Yan, Shirui Pan, Xiaochun Ye, Dongrui Fan
AAAI 2023 | Venue PDF | 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 EMAIL, EMAIL, EMAIL |
| 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. |