DeeperGCN:训练使用通用聚合函数的深度GCN
IEEE transactions on pattern analysis and machine intelligence
|August 21, 2023
概括
深度图形神经网络 (GNN) 对聚合函数敏感. 本研究介绍了通用聚合函数,提高了在大型图形学习任务上的深度GNN性能.
科学领域:
- 机器学习 机器学习
- 图形表示学习学习学习图形表示学习
背景情况:
- 图形神经网络 (GNN) 越来越多地用于在图形结构数据上的表示学习.
- 最近的进展使得训练非常深入的GNN成为可能,在点云学习和蛋白质相互作用预测等领域取得了成功.
- 在大尺度图表上深度GNN的性能仍然是研究的一个领域.
研究的目的:
- 调查聚合函数选择对深度GNN在大型图设置中的性能的影响.
- 针对GNN对不同数据集中的标准聚合函数 (平均值,最大值,和值) 的敏感性.
- 提出一种新的,可学习的聚合函数类,以提高深度GNN性能.
主要方法:
- 系统地研究聚合函数对深层GNN的影响.
- 介绍了通用聚合函数,这是一个新的类别的变不变和可微分函数.
- 针对特定任务量身定制的通用聚合函数参数的端到端学习.
主要成果:
- 在选择聚合函数时,GNN表现出显著的敏感性.
- 一般化聚合函数扩展现有方法,提供更大的灵活性和适应性.
- 配备了通用聚合函数的深度残留GNN可以在开放图基准 (OGB) 数据集上获得最先进的结果.
结论:
- 聚合函数的选择对于深度GNN性能至关重要,特别是在大规模的场景中.
- 一般化聚合函数提供了一个强大的和可适应的解决方案来改进深度GNN.
- 这项工作通过提供更有效的聚合方法来推进对图形的深度学习.
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