这些深度学习方法基于图神经网络(graph neural network),大致的思想都是将图上的节点映射为稠密向量,然后通过计算两个向量之间的夹角来估计两个节点之间的距离。 使用随机游走的方法计算节点之间的距离至少在1993年就已经有文章提出了,例如文章Resistance distance和Random Walks on Graphs: A Survey. 该算法的思想是,为...
0、简介 论文名字:Random Walk Graph Neural Networks 下载地址:https://www.lix.polytechnique.fr/~nikolentzos/files/rw_gnns_neurips20 会议:NIPS2020 1、论文的motivation 图神经网络可以学习图的特征,从而进行图分类的任务。目前,主流的图神经网络算法都是MPNN的结构。这种结构的... ...
Deep Neural Networks for Learning Graph Representations stack anto-encoder 的相关介绍 网络性质 属性、标签 权重 方向 方法 任务 同质 有 有权 有向 matrix factorization node classification 异质 无 无权 无向 random walk link prediction / / /... ...
The development of Graph Neural Networks (GNNs) has led to great progress in machine learning on graph-structured data. These networks operate via diffusing information across the graph nodes while capturing the structure of the graph. Recently there has also seen tremendous progress in quantum ...
random-walkstate-space-modelsgraph-neural-networksgraph-representation-learninggraph-transformer UpdatedJun 6, 2024 Python hiyouga/RepWalk Star25 Code Issues Pull requests Code and dataset for our paper "Replicate, Walk, and Stop on Syntax: an Effective Neural Network Model for Aspect-Level Sentiment...
《Graph Random Neural Networks for Semi-Supervised Learning on Graphs》的读书笔记 厦明大 DOTA2人机GG君,想去画画唱民谣弹吉他的码农18 人赞同了该文章 一、摘要 目前的GNN方面的方法在半监督分类上,普遍存在着:(1)特征向量过平滑;(2)对扰动的鲁棒性不够 的问题,因此本文提出一种GRAND的算法,算法分为两个...
We tested our idea only on random-work algorithms, but it can be easily extended to other network-based algorithms, especially graph neural networks. Although most graph neural networks are based on message-passing architectures, there exist such cases where random walk is directly used, such as...
Convolutional neural networks architectures are an attractive option for parameterization, as their dimensionality is small and does not scale with network size. We introduce the random edge graph neural network (REGNN), which performs convolutions over random graphs formed by the fading interference ...
本文提出一种新型 GNN 架构 Graph Random Neural Networks(GRAND),GRAND 用于优化 GNN 中 over-smoothing,non-robustness 和在标签节点稀疏时所存在的 weak-generalization 问题。其主要有两个部分组成:基于随机传播策略的图数据增强,基于一致性正则化的无标签节点预测优化。 GRANDarxiv.org/pdf/2005.11079.pdf 1....
一个简单而有效的框架- GRAPH RANDOM NEURAL NETWORKS ( GRAND )来解决这些问题。 在GRAND中, 我们首先设计了一种随机传播策略来进行图数据增强。 然后,我们利用一致性正则化来优化不同数据增强中无标签节点的预测一致性。在图基准数据集上的大量实验表明,GRAND在半监督节点分类上显著优于当前最先进的GNN基线。 最后...