HOW POWERFUL ARE GRAPH NEURAL NETWORKS?

本文深入探讨了图神经网络(GNN)的表示能力,分析了它们在捕捉不同图形结构方面的潜力。研究发现GNN在区分图结构方面与Weisfeiler-Lehman图同构测试相当。此外,提出了一种名为图同构网络(GIN)的简单架构,其表征能力等同于WL测试。实验验证了理论,表明GIN在图分类任务中表现出最先进的性能,而某些GNN变体如GCN和GraphSAGE则无法区分特定的图结构。
HOW POWERFUL ARE GRAPH NEURAL NETWORKS?

ABSTRACT

Graph Neural Networks (GNNs) are an effective framework for representation learning of graphs. GNNs follow a neighborhood aggregation scheme, where the representation vector of a node is computed by recursively aggregating and trans-forming representation vectors of its neighboring nodes. Many GNN variants have been proposed and have achieved state-of-the-art results on both node and graph classification tasks. However, despite GNNs revolutionizing graph representation learning, there is limited understanding of their representational properties and limitations. Here, we present a theoretical framework for analyzing the expressive power of GNNs

评论
添加红包

请填写红包祝福语或标题

红包个数最小为10个

红包金额最低5元

当前余额3.43前往充值 >
需支付:10.00
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包

打赏作者

Adam婷

你的鼓励将是我创作的最大动力

¥1 ¥2 ¥4 ¥6 ¥10 ¥20
扫码支付:¥1
获取中
扫码支付

您的余额不足,请更换扫码支付或充值

打赏作者

实付
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值