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..
Restructuring Graph for Higher Homophily via Adaptive Spectral Clustering
Authors: Shouheng Li, Dongwoo Kim, Qing Wang
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results show that our graph restructuring method can significantly boost the performance of six classical GNNs by an average of 25% on less-homophilic graphs. |
| Researcher Affiliation | Academia | 1 School of Computing, Australian National University, Canberra, Australia 2 CSE & GSAI, POSTECH, Pohang, South Korea 3 Data61, CSIRO, Canberra, Australia |
| Pseudocode | Yes | A detailed algorithm can be found in Appendix. |
| Open Source Code | Yes | The code is available at https://github.com/seanli3/graph_restructure. |
| Open Datasets | Yes | We evaluate on four real-world graphs: TEXAS, CORNELL, CHAMELEON and SQUIRREL (Rozemberczki, Allen, and Sarkar 2021), as well as synthetic graphs of controlled homophily. |
| Dataset Splits | Yes | We adopt early stopping and record the results from the epoch with highest validation accuracy. We use the same split setting as Pei et al. (2020); Zhu et al. (2020). |
| Hardware Specification | Yes | All experiments are run on a single NVIDIA RTX A6000 48GB GPU unless otherwise noted. |
| Software Dependencies | No | The paper does not provide specific software dependency versions (e.g., library or framework names with version numbers). |
| Experiment Setup | Yes | Hyperparameters are tuned using grid search for all models on the unmodified and restructured graphs of each dataset. For the spectrum slicer in Equation 10, we use a set of 20 slicers with s = 40 and m = 4 so that the spectrum is sliced into 20 even range of 0.1. |