Therefore, we present a spatial-temporal gated graph attention network (ST-GGANet) to learn the spatial-temporal patterns of skeleton sequences. The proposed approach uses a lightweight self-attention-based gate layer to pay attention to the important body parts or joints of human skeleton ...
之前的图网络学习算法系列中,我们已经总结了如传统的Deepwalk,以及以卷积图神经网络为基础的GCN,GAT和GraphSAGE方法。今天,我们来学习下Graph Neural Network中的另一大类型,利用门控信息来进行更新的Gated G…
链接:《Gated Graph Sequence Neural Networks》 Introduction 图结构数据在实际生活中往往很常见,在化学、自然语言处理、社交网络、知识库等应用中,都存在大量的图结构数据。这些应用主要可以分为两大类:一类是graph-focused,另一类则是node-focused。Graph-focused应用往往关注整个图上的信息,这一类应用有化学组成研究、...
The manuscript entitled “Gated Tree-based Graph Attention Network (GTGAT) for Medical Knowledge Graph Reasoning.” Acknowledgment This study was supported in part by a grant from National Natural Science of China [62006063] and the Heilongjiang Provincial Postdoctoral Science Foundation (CN) [LBH-...
To address the above mentioned issues, a physics-informed gated recurrent graph attention unit network (PGRGAT) is proposed, which consists of two co-trained components: a physics-informed graph structure learning module (PGSL) and a gated recurrent graph attention unit (GRGAU) network. To learn...
Graph convolutional neural network can capture the local spatial correlation between adjacent nodes in graph, but different adjacent points have different impact on the current node. The key idea of spatial attention mechanism is to pay adaptive attention to the characteristics of the most relevant nod...
Bhatti UA, Huang M, Neira-Molina H, Marjan S, Baryalai M, Tang H, Wu G, Bazai SU (2023) MFFCG – Multi feature fusion for hyperspectral image classification using graph attention network. Expert Syst Appl 229 (Part A):120496 Fodstad M, del Granado PC, Hellemo L, Knudsen BR, Pisci...
论文笔记|Document Modeling with Graph Attention Networks for Multi-grained Machine Reading Comprehension 作者:迪 单位:燕山大学 论文地址 代码地址 论文来源:ACL2020 前言 由于最近的工作想要利用图结构解决问题,因此分享此文的目的是想与大家探讨如何使用图结构表达文章信息。 概述 机器阅读理解是模型...
论文笔记:A Gated Self-attention Memory Network for Answer Selection,程序员大本营,技术文章内容聚合第一站。
(i.e., the positive and negative correlation) between node features simultaneously even though we use attention mechanisms like Graph Attention Network (GAT), since the weight calculated by attention is always a positive value. In this paper, we propose a novel ...