3. GNNs with Heterophily 通过消息传递机制的视角,现有异染的卫星系统可以分为两组:(1) Non-local neighbor extension methods; (2) GNN architecture refinement methods。这两个类别相应地解决了两个关键问题: (1)如何发现合适的邻居; (2)如何利用发现的邻居的信息。 Non-local neighbor extension methods 分...
Graph Neural Networks with Heterophily.Jiong ZhuRyan A. RossiAnup B. RaoTung MaiNedim LipkaNesreen K. AhmedDanai KoutraNational Conference on Artificial Intelligence
Homophilic & Heterophilic Link Prediction Encoder & Decoder Choices for Link Prediction Beyond Homophily Introduction 本文贡献 异质性链接预测的定义:我们引入正式定义的异质性链接预测任务:而不是依赖特征相似性的大小,我们的定义是基于特征相似性得分的分离边缘和非边缘,这是合理的一个简洁的理论框架,强调了不同...
Revisiting heterophily for graph neural networks. NIPS, 2022.概介绍了一种新的 graph homophily metrics.符号说明G=(V,E,A)G=(V,E,A), graph; |V|=N|V|=N; A∈RN×NA∈RN×N, adjacency matrix; Ni={j:eij∈E}Ni={j:eij∈E}, neighborhood set; X∈RN×FX∈RN×F, feature matrix; Z...
Diversification Helps with Harmful Heterophily 4.1节用图3展示了为什么高通滤波器有效果 图3 可以看到,图3中右边的图的矩阵,有正有负,比较有区分度。而图3中间的矩阵,数值都比较均衡,看不出区分度,效果不好。因此,高通滤波器对异质性是有帮助的。
We propose a Global-Representation-based attention mechanism for graph neural network. • The low-rank mechanism provides structural information on neighbor nodes. • We address over-smoothing and heterophily issues by various compositions of mechanism. • We justify its effectiveness and superiorit...
Graph Neural Networks (GNNs) are widely used on a variety of graph-based machine learning tasks. For node-level tasks, GNNs have strong power to model the homophily property of graphs (i.e., connected nodes are more similar) while their ability to...
的时候, 我们称这个图是偏 homophily 的, 否则则这个图是偏 heterophily 的. 注意: heterophily 和常说的 Heterogeneity (异构图) 有点像, 后者描述的是一个图的结点的 type 不一致 (≥2≥2), 比如一个图中存在 (users, watch, movies) 的关系. 前者描述了 group 的一个性质, 显然当 h→1h→1 的...
Graph neural networks (GNNs) are designed to exploit both sources of evidence but they do not optimally trade-off their utility and integrate them in a manner that is also universal. Here, universality refers to independence on homophily or heterophily graph assumptions. We address these issues ...
We therefore propose a novel Structure-aware Path Aggregation Graph Neural Network (PathNet) aiming to generalize GNNs for both homophily and heterophily graphs. Specifically, we first introduce a maximal entropy path sampler, which helps us sample a number of paths containing structural context. ...