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...
Beyond Homophily in Graph Neural Networks: Current Limitations and Effective DesignsJiong ZhuYujun YanLingxiao ZhaoDanai Koutra
Dual Graph Clustering Network Experiments Keywords: Graph Clustering, Homophily Introduction 虽然异质性方法提高了gnn在一些下游任务,存在两个关键问题: 1)定制网络的训练,自适应过滤器的学习和图重组方法依赖于标记样本,这使得它们不适用于聚类任务。 2)gnn将原始数据嵌入到单个子空间中,其中属性与拓扑结构的耦合加重...
We investigate the representation power of graph neural networks in the semi-supervised node classification task under heterophily or low homophily, i.e., in networks where connected nodes may have different class labels and dissimilar features. Many popular GNNs fail to generalize to this setting,...
真实的图数据可能具有不同程度的同配性(Homophily),本文目的是提出一个图无关的深度聚类方法(聚类属于无监督学习,并没有标签来判断一个图是同配的还是异配的)。提出的方法包括三个关键组成部分:图重构、混合滤波器和双图聚类网络。首先根据两种无监督图构建策略分别提取同配和异配信息;然后使用混合滤波器平衡数据的...
PathNet is a structure-aware path aggregation graph neural network that can deal with both homophily and heterophily graphs. This implementation of PathNet is based onPytorch GeometricAPI. Building the Project splits: need to unzipped, contains the split data of "Cora, Cornell, Pubmed and Citese...
Graph neural networks (GNNs) have been extensively studied for prediction tasks on graphs. Aspointed out by recent studies, most GNNs assume local homophily, i.e., strong similarities in localneighborhoods. This assumption however limits the generalizability power of GNNs. To address thislimitation,...
homophilyheterophilyUnsupervised graph representation learning (GRL) aims at distilling diverse graph information into task-agnostic embeddings without label supervision. Due to a lack of support from labels, recent representation learning methods usually adopt self-supervised learning, and embeddings are ...