HGEN: Heterogeneous Graph Ensemble Networks
Authors: Jiajun Shen, Yufei Jin, Kaibu Feng, Yi He, Xingquan Zhu
IJCAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on five heterogeneous networks validate that HGEN consistently outperforms its state-of-the-art competitors by substantial margin. Empirical study on five real-world heterogeneous networks validates the effectiveness of HGEN over the recent arts |
| Researcher Affiliation | Academia | 1Dept. of Electrical Engineering and Computer Science, Florida Atlantic University, USA 2Department of Data Science, William & Mary, USA EMAIL; EMAIL |
| Pseudocode | Yes | Main steps of HGEN are outlined in Algorithm 1 in Appendix. |
| Open Source Code | Yes | Codes are available at https://github.com/Chrisshen12/HGEN. |
| Open Datasets | Yes | Experiments on five heterogeneous networks validate that HGEN consistently outperforms its state-of-the-art competitors by substantial margin. Five heterogeneous graphs from real applications are used as benchmark datasets. Their statistics and detailed descriptions are deferred to Supplement B of Appendix due to page limits. |
| Dataset Splits | No | The paper mentions using five benchmark datasets but does not explicitly state the train/test/validation splits or their percentages, nor does it refer to standard splits with citations. It mentions training but lacks details on data partitioning for reproducibility. |
| Hardware Specification | Yes | All experiments are run on desktop workstations equipped with Nvidia Ge Force RTX 2080 Ti. |
| Software Dependencies | No | We choose Adam [Kingma and Ba, 2014] as our optimizer. - This only mentions an optimizer by name, not with a specific version number, nor does it list any other software dependencies with versions. |
| Experiment Setup | No | We perform a grid search with selected range of hyperparameters including hidden dimension, layer size, dropping rate, number of individual GNN, and control rate for regularizer. We choose Adam [Kingma and Ba, 2014] as our optimizer. We fix the learning rate, weight decay, the number of epochs and apply early stopping mechanism. - While it mentions types of hyperparameters and mechanisms like early stopping, it does not provide specific values or ranges for these hyperparameters (e.g., the actual learning rate value, batch size, number of epochs, or the specific range for hidden dimension). |