论文:《HOW POWERFUL ARE GRAPH NEURAL NETWORKS? 》 ABSTRACT 图神经网络(GNNs)是一种有效的图表示学习框架。GNN遵循邻域聚合方案,通过递归聚合和转换邻域节点的表示向量来计算节点的表示向量。许多GNN变体已经被提出,并且在节点和图分类任务上都取得了最新的结果。然而,尽管GNNs给图形表示学习带来了革命性的变化,但是...
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 transforming representation vectors of its neighboring nodes. Many GNN variants...
通过GIN 学习的节点嵌入可以直接用于节点分类问题以及链路预测问题,但对于图分类问题,我们提出了以下方法:通过节点的嵌入构造整个图的嵌入。一般我们会把这个过程称为Graph Readout。 Graph Readout 的一个重要因素是:随着迭代(聚合)次数增加,对应于子树结构的节点表征会变得更加精细以及覆盖范围更广。因此足够的迭代次数...
论文标题:How Powerful are Graph Neural Networks论文作者:Keyulu Xu, Weihua Hu, J. Leskovec, S. Jegelka论文来源:2019, ICLR论文地址:download 论文代码:download 1 IntroductionGNN 目前主流的做法是递归迭代聚合一阶邻域表征来更新节点表征,如 GCN 和 GraphSAGE,但这些方法大多是经验主义,缺乏理论去理解 GNN ...
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 transforming representation vectors of its neighboring nodes. Many GNN variants...
Graph Neural Networks (GNNs) have known an important and fast development in the last decade, with many theoretical and practical innovations. Their main feature is the capability of processing graph structured data with minimal loss of structural information. This makes GNNs the ideal family of ...
本文介绍的论文是《How Powerful are Graph Neural Networks?》。 作者提出了一个新的图神经网络框架GIN来捕捉图结构信息,经作者测试该GNN是最具表现力的,能够与Weisfeiler - Lehman图同构测试一样强大,在多个图分类基准上达到了最先进的性能。 🍁一、背景 ...
图神经网络（GNN）通过聚合节点与其邻居的特征信息进行计算，通过多轮迭代，每个节点的表示向量包含了整个图的结构信息。GNN的每一层计算方式包括了聚合（AGGREGATE）和结合（COMBINE）节点特征的过程。关键在于聚合函数和读取函数的设计，若它们是单射，且层数足够多，GNN能将非同构图映射到不同的表示空间。
GNNs的聚合框架可以看作是multiset函数。 multiset:包含重复元素的集合。 聚合: 如果有两层聚合,无论是GCN还是WL test,以蓝色节点为例,先由其二阶邻居信息聚合得到一阶邻居节点信息,再由一阶邻居信息聚合得到自己。 WL test 是由单射函数(hash,不同输入得到不同输出)聚合 ...
Thinking of the neural network’s output as a single number allows us to think about its performance in simple terms. The goal is to find the series of weights that results in the lowest loss value, or the minimum. Plotting this on a graph, as in Figure 2, shows that the Loss ...