Výsledky vyhledávání

Report

CAT: A Causally Graph Attention Network for Trimming Heterophilic Graph

Autor: He, Silu, Luo, Qinyao, Fu, Xinsha, Zhao, Ling, Du, Ronghua, Li, Haifeng

Publikováno v: Information Science 2024

Local Attention-guided Message Passing Mechanism (LAMP) adopted in Graph Attention Networks (GATs) is designed to adaptively learn the importance of neighboring nodes for better local aggregation on the graph, which can bring the representations of s

Externí odkaz: http://arxiv.org/abs/2312.08672

Zobrazit plný text záznamu

Report

Alleviating neighbor bias: augmenting graph self-supervise learning with structural equivalent positive samples

Autor: Zhu, Jiawei, Hong, Mei, Du, Ronghua, Li, Haifeng

In recent years, using a self-supervised learning framework to learn the general characteristics of graphs has been considered a promising paradigm for graph representation learning. The core of self-supervised learning strategies for graph neural ne

Externí odkaz: http://arxiv.org/abs/2212.04365

Zobrazit plný text záznamu

Report

STGC-GNNs: A GNN-based traffic prediction framework with a spatial-temporal Granger causality graph

Autor: He, Silu, Luo, Qinyao, Du, Ronghua, Zhao, Ling, Li, Haifeng

The key to traffic prediction is to accurately depict the temporal dynamics of traffic flow traveling in a road network, so it is important to model the spatial dependence of the road network. The essence of spatial dependence is to accurately descri

Externí odkaz: http://arxiv.org/abs/2210.16789

Zobrazit plný text záznamu