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. 2025 Aug 11;16:1596408. doi: 10.3389/fneur.2025.1596408

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Copyright © 2025 Yu and Zhu.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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Line graphs display the impact of GCN layer depth, attention dimension, and regularization weight on model performance. Each graph shows accuracy, recall, and F1 score with peaks at certain values: 3, 128, and 0.5, respectively. — Parameter sensitivity analysis curves. Each plot shows how the model's accuracy, recall, and F1 score vary with respect to one hyperparameter: (Left) GCN layer depth, (Middle) attention dimension, and (Right) domain regularization weight. Optimal values were found at three GCN layers, 128 attention dimensions, and 0.5 regularization weight.