位置感知图神经网络:主动学习节点相对位置的位置
IEEE transactions on neural networks and learning systems
|March 26, 2024
概括
现有的图形神经网络 (GNN) 在节点定位方面遇到了困难. 位置感应GNN (PSGNN) 学习最佳选,显著改善图表表示学习和节点分类和链接预测任务的性能.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 图形神经网络 图形神经网络
背景情况:
- 图形神经网络 (GNN) 通常使用消息传递,但缺乏对相对节点位置的固有理解.
- 现有的位置感知GNN (P-GNN) 使用固定,这可能会损害位置感知和特征提取能力.
- 对于全面的图形嵌入来说,最佳的选是一个NP完全问题,阻碍了确定性算法解决方案.
研究的目的:
- 开发一种新的图形神经网络 (GNN) 架构,能够学习图形中的相对节点位置.
- 解决现有的位置感知GNN (P-GNN) 中任意选的局限性.
- 提出一种高效且可扩展的方法,以提高GNN的位置意识,而不屈服于NP完整性挑战.
主要方法:
- 引入了位置感知GNN (PSGNN),通过可反向传播的方法来学习选.
- 证明了均分布和不对称的对有效的位置意识的重要性.
- 在各种合成和现实世界的图形数据集上与最先进的GNN验证了拟议的方法.
主要成果:
- 与现有方法相比,PSGNN显著提高了对联节点分类和链接预测任务的性能.
- 在对联节点分类中实现了平均曲线下面面积 (AUC) 提升超过14%.
- 链接预测的平均AUC改善超过18%,显示了显著的收益.
- 在各种图形数据集中表现出稳定的可扩展性.
结论:
- PSGNNs通过以可反向传播的方式自适应地选择,从而有效地学习相对节点位置.
- 提出的方法克服了与最佳选相关的NP完整性问题.
- PSGNNs在GNN中提供了有前途的进步,提高了它们捕捉复杂图形结构和关系的能力.
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