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...
Recently, heterophilous graph neural networks (HeterGNNs) have attracted increasing attention by modifying the neural message passing schema for heterophilous neighborhoods. However, they suffer from insufficientneighborhood partitionandheterophily modeling, both of which are critical but challenging to ...
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...
图神经网络(GNN)通过使用基于关系归纳偏差(同质性假设)的图结构来扩展神经网络(Neural Network,NN)。虽然人们普遍认为GNN在实际任务中优于NN,但最近的工作发现了一组非平凡的数据集(异质图数据集),与NN相比,GNN的性能并不令人满意。异质性被认为是GNN性能不好的主要原因,并提出了许多工作来解决这一问题。在本文中...
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...
Graph neural networks with heterophily Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35 (2021), pp. 11168-11176 CrossrefGoogle Scholar [12] C. Cai, Y. Wang A note on over-smoothing for graph neural networks arXiv preprint arXiv:2006.13318 (2020) Google Scholar [13] ...
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...
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 capture ...
Official implementation of NeurIPS 2023 paper "Predicting Global Label Relationship Matrix for Graph Neural Networks under Heterophily" - Jinx-byebye/LRGNN
Graph neural networks (GNNs) have been intensively studied in various real-world tasks. However, the homophily assumption of GNNs' aggregation function limits their representation learning ability in heterophily graphs. In this paper, we shed light on the path level patterns in graphs that can ...