Restructuring Graph for Higher Homophily via Adaptive Spectral Clustering
Authors: Shouheng Li, Dongwoo Kim, Qing Wang
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | 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. |