可以看到,path和queue之间的方向,即path to queue和queue to path是不同的两种单向关系。 将其处理为dglgraph的形式: data_dict={('queue','queue_to_link','link'):(x['queue_to_link'].to(torch.int32),x['sequence_links'].to(torch.int32)),('path','path_to_queue','queue'):(x['path_...
文章于2020年七月发表在IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING上,第一作者为范文琪,近几年主要研究方向为graph,social recommendat,llm等,有多篇结合图神经网络进行社交分析的文章。作者介绍 1.概述 社交网络、用户购物行为、物品间关系等许多现实应用中的数据都可以用图来表示。图神经网络( Graph Neur...
based on a graph neural network architecture, uniting structural and functional information observed with DTI and fMRI. First this model provides a data-driven perspective on a fundamental question in neuroscience, namely how the function of the brain is related to its structure. Moreover, by model...
例如,道路网络自然是一个graph,以道路交叉点为节点,道路连接点为边。以graph作为输入,一些基于gnn的模型在包括道路交通流和速度预测问题的任务中显示出比以前的方法更好的性能。 例如,这些模型包括扩散卷积递归神经网络(DCRNN) (Li等人,2018b)和Graph WaveNet (Wu等人,2019)模型。基于gnn的方法也被扩展到其他交通方...
用于社交推荐的数据也可以用用户-用户社交图(user-user social graph)和用户-项目图(user-item graph)的形式表示为图形数据。此外,项目之间的关系可以表示为图形数据,表示为项目项目图。gnn为推进社会推荐提供了前所未有的机会,然而,在基于这种模型框架构建基于gnn的社会推荐存在相当大的挑战,因为(1)用户(物品)同时...
application scenarios, as we cannot assume that all local and remote IP addresses and port numbers in the network are known at training time. In contrast, the EGraphSAGE approach presented in this paper uses an inductive graph neural learning approach, which does not suffer from this limitation....
a graph neural network-based bearing fault detection (GNNBFD) method. The method first constructs a graph using the similarity between samples; secondly the constructed graph is fed into a graph neural network (GNN) for feature mapping, and the samples outputted by the GNN network fuse the ...
application scenarios, as we cannot assume that all local and remote IP addresses and port numbers in the network are known at training time. In contrast, the EGraphSAGE approach presented in this paper uses an inductive graph neural learning approach, which does not suffer from this limitation....
本文是对文献 《Graph Neural Networks: A Review of Methods and Applications》 的内容总结,详细内容请参照原文。 引言 大量的学习任务都要求能处理包含丰富的元素间关联关系的图数据,例如物理系统建模、疾病分类以及文本和图像等非结构数据的学习等。
2.1 找到图结构(Find graph structure) 首先,我们必须找出应用程序中的图结构。通常有两种场景:结构场景和非结构场景。在结构场景中,图结构在应用中是明确的,例如在分子、物理系统、知识图谱等方面的应用。在非结构场景中,图是隐式的,因此我们必须首先从任务中构建图,例如为文本构建全连接的“单词”图或为图像构建...