Kecheng Cai, Chenyang Xu, Chao Peng
The paper proposes a self-reflection framework to improve graph interpretability by reducing spurious correlations, specifically targeting the challenging Spurious-Motif benchmark datasets.
This research addresses the challenge of making machine learning models better at understanding graphs by focusing on a specific problem: spurious correlations. These are misleading patterns that can confuse models, especially in a tough dataset called the Spurious-Motif benchmark. The authors have adapted a technique called self-reflection, originally used in language models, to help graph models identify truly important parts of a graph. This approach involves re-evaluating the model's initial decisions to improve accuracy and understanding.
