图形聚合-排斥网络:不要信任异性图形中的所有邻居
Yuhu Wang1, Jinyong Wen1, Chunxia Zhang2
1State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China.
概括
本研究介绍了GARN,这是一种新的图形神经网络框架,可以有效地捕获图形数据中的同型和异型信息. GARN使用独特的图形聚合-排斥卷积机制来提高节点和图形分类任务的性能.
科学领域:
- 机器学习 机器学习
- 图形理论 图形理论
- 网络分析 网络分析
背景情况:
- 图形神经网络 (GNN) 在同型图形上表现出色,但在节点具有不同特征的异型图形上扎.
- 异构图的现有方法往往忽略了有价值的信息或是无效的,导致下游任务的性能差.
- 这凸显了对能够处理多样化的图形结构的高级GNN架构的需求.
研究的目的:
- 提出一个新的框架,GARN,从图形数据中有效提取同型和异型信息.
- 解决当前GNN在处理异性图形中的局限性.
- 为了增强图形表示学习,用于诸如节点和图形分类等任务.
主要方法:
- 使用光谱和空间理论分析异性图形处理中GNN缺陷的分析.
- 图形聚合-排斥卷积 (GARC) 机制的设计,用于融合低通和高通图形过器.
- 实施GARC以正负注意力权重,分别聚合相似和排斥不相似的节点,由可学习的整合权重控制.
主要成果:
- 拟议的GARN框架,通过堆叠GARC层,证明了在图形表示学习中的有效性.
- 对同型,异型和图像转换图的实验显示,与现有的GNN基线相比,性能优越.
- GARC机制以适应性平衡聚合和排斥,防止过度依赖类内相似性.
结论:
- GARN有效地提取同型和异型信息,在各种图形数据集上表现优于基线GNN.
- GARC机制为图形过提供了一种灵活的方法,可以适应不同的图形属性.
- 该框架显示了在复杂的图形结构上的节点和图形分类任务中提高性能的巨大潜力.
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