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图形卷积网络与自适应分组聚合策略
Ruixiang Wang1, Chunxia Zhang2, Chunhong Pan3
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
图形卷积网络 (GCNs) 与天真聚合作斗争. 我们的自适应分组聚合 (AGA) 策略增强了节点信息保留和特征歧视,改善了GCN的性能.
科学领域:
- 图形神经网络的神经网络
- 机器学习 机器学习
- 网络科学 网络科学
背景情况:
- 图形卷积网络 (GCNs) 由于天真节点聚合函数而面临性能瓶,限制了它们的理论表达力和实际应用.
- 现有的基于学习的聚合策略缺乏对表达力和标准化实验评估的关注.
- 纯粹的聚合函数无法保留足够的节点信息,导致较少的区分特征和性能差距.
研究的目的:
- 解决GCN中天真聚合函数的局限性.
- 提出一种新的聚合策略,增强节点信息保留和特征歧视.
- 提高GCN的理论表达力和实际性能.
主要方法:
- 引入了自适应分组聚合 (AGA),灵感来自韦斯菲勒-莱曼 (WL) 测试的标签直方图.
- 在节点特征和可学习组标签之间使用修改的学生t分布开发了一个分组机制.
- 实现了AGA战略作为一个端到端可训练的管道,使用Gumbel Softmax进行无集成到GCN架构中.
主要成果:
- 通过保留更全面的节点信息,AGA策略显著增强了特征歧视.
- 在多个基准上的实验表明,与其他聚合策略相比,所有对照组的绩效都得到了持续的改善.
- 在大多数实验环境中,包括大规模基准,AGA取得了最先进的结果.
结论:
- 拟议的自适应分组聚合 (AGA) 有效地克服了GCN中天真聚合函数的局限性.
- AGA提供了一个强大而灵活的插件模块,可以明显提高GCN的性能和表现力.
- 该方法的优越性通过广泛的实验和与现有最先进的方法进行比较来验证.
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01:29Aggregates Classification
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