Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Escaping Feature Twist: A Variational Graph Auto-Encoder for Node Clustering
Authors: Nairouz Mrabah, Mohamed Bouguessa, Riadh Ksantini
IJCAI 2022 | Venue PDF | LLM Run Details | Input Tokens: 15,985 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,871 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct various experiments to validate the contributions of the proposed approach. The obtained results provide strong evidence that our model can improve the clustering performance over several state-of-the-art deep graph clustering methods by escaping Feature Twist. |
| Researcher Affiliation | Academia | Nairouz Mrabah1 , Mohamed Bouguessa1 and Riadh Ksantini2 1University of Quebec at Montreal, Montreal, QC, Canada 2University of Bahrain, Kingdom of Bahrain |
| Pseudocode | No | The full algorithm and its complexity analysis are provided in Appendix F and Appendix G, respectively, due to page limit restrictions. |
| Open Source Code | Yes | Our code is available on https://github.com/nairouz/FT-VGAE. |
| Open Datasets | Yes | We select six datasets for our experiments: three citation networks [Sen et al., 2008] (Cora, Citeseer, and Pubmed) and three air traffic networks [Sen et al., 2008] (Brazil Air Traffic, US Air Traffic, and Europe Air Traffic). |
| Dataset Splits | No | No explicit dataset splits (e.g., percentages, counts) for training, validation, or testing were found in the main text of the paper. Details are deferred to Appendix I. |
| Hardware Specification | No | All experiments are performed under the same hardware and software environments as described in Appendix J. Specific hardware details are not provided in the main text. |
| Software Dependencies | No | All experiments are performed under the same hardware and software environments as described in Appendix J. Specific software dependencies with version numbers are not provided in the main text. |
| Experiment Setup | No | The architecture, learning rates, and all the other hyper-parameters of FT-VGAE are specified and discussed in Appendix H. These details are not in the main text. |