对GNN可解释性的新方法:通过层间对齐来蒸知识
IEEE transactions on pattern analysis and machine intelligence
|December 17, 2025
概括
我们开发了一个更简单的代理模型来解释复杂的图形神经网络 (GNN). 这种方法使用知识蒸与层间对齐,使GNN解释更加透明和高效.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 网络科学 网络科学
背景情况:
- 图形神经网络 (GNN) 在网络数据分析方面表现出色,但存在"黑子"问题,阻碍了信任和应用.
- 现有的GNN解释方法通常是复杂和昂贵的,因为它们依赖子图选择和组合优化.
- 在GNN中过度平滑进一步复杂化了模型的解释性和解释生成.
研究的目的:
- 为解释 GNN 决策流程开发一个较低复杂度的代理模型.
- 在复杂网络分析中提高GNN模型的透明度和可信度.
- 为应对高解释成本和过度平滑对GNN可解释性的影响所带来的挑战.
主要方法:
- 引入了一个从复杂的GNN中获得的代理模型,使用知识蒸.
- 在蒸过程中使用层间对齐,以确保代理模型与原来的GNN保持一致.
- 从理论上证明了代理模型对两个模型生成的解释的忠实性.
主要成果:
- 提出的方法有效地将复杂的GNN的见解提炼成一个可管理的代理模型.
- 层间对齐成功地减轻了过度平滑效应,提高了解释质量.
- 在真实世界数据集上的实验结果证明了拟议的解释技术的有效性和稳定性.
结论:
- 开发的代理模型为解释GNN提供了更透明和更有效的方法.
- 以层间对齐的知识蒸是提高GNN可解释性的可行策略.
- 该方法提供了可靠和强大的解释,为更广泛的GNN采用铺平了道路.
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