Graph Neural Networks Measure of Heterophily & Homophily 3. GNNs with Heterophily 3.1 Non-local Neighbor Extension 3.2 GNN Architecture Refinement 4. Real-World Benchmarks 5. Future Directions 总结 论文地址:arxiv.org/pdf/2202.0708 日期:2022年2月 Abstract 近年来,图神经网络(GNNs)得到了快速的发展...
Graph Neural Networks with Heterophily.Jiong ZhuRyan A. RossiAnup B. RaoTung MaiNedim LipkaNesreen K. AhmedDanai KoutraNational Conference on Artificial Intelligence
论文题目是“Multi-Track Message Passing: Tackling Oversmoothing and Oversquashing in Graph Learning via Preventing Heterophily Mixing”。该工作的主要贡献包括, 提出了一种新颖的多轨道消息传递方案MTMP,通过避免异质混合(Heterophily Mixing)有效解决了过度平滑和过碾压问题。 从图学习和半监督学习的角度阐明了M...
基于这一调查,我们证明了一些有害的图异质性情况可以通过局部多样化操作(local diversification operation)得到有效解决。然后,我们提出了自适应信道混合(Adaptive Channel Mixing, ACM),这是一个框架,可以灵活地针对每个节点(node-wisely)利用聚合、多样化和恒等通道,为不同节点的异质性情况提取更丰富的局部化信息。ACM比...
Keywords: Heterophily, Graph Neural Networks, Node Classification Introduction 关系归纳偏差被认为是导致gnn在许多任务中优于其他神经网络表现的关键因素。然而,越来越多的证据表明,在处理关系数据时,gnn并不总是比传统的nn获得优势。在某些情况下,即使是简单的多层感知器(MLPs)也能比gnn更出色,该原因被归咎为异质...
重新思考图神经网络为何会在异配图上有严重的性能下降(Revisiting Heterophily For Graph Neural Networks...
Graph Neural Networks (GNNs) have proven to be useful for many different practical applications. However, most existing GNN models have an implicit assumption of homophily among the nodes connected in the graph, and therefore have largely overlooked the important setting of heterophily. In this wor...
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...
6 开放性结尾：图神经网络在处理异配(Heterophilic)图时面临的性能挑战确实是目前研究领域活跃探讨的问题...
Node2Vec是DeepWalk的扩展,它通过调整随机游走策略,能够在节点嵌入中更好地平衡“同质性”(Homophily)和“异质性”(Heterophily)两种关系。同质性指的是图中相似节点倾向于连接在一起,而异质性则指的是图中不同类型的节点也可能有连接。 2.3.1Node2Vec算法特点 ...