本文主要参考资料为CS224W的Lecture 6 & 7,课程视频可在Youtube观看。 Graph Neural Networks Deep Learning for Graphs 首先假设我们有一个图G,其邻接矩阵为A,特征矩阵为X。对于图中的节点v,用Nv表示其邻居节点的集合。 图神经网络的思想是对于节点v,先对其邻居节点的特征向量进行一些滤波操作再将其结合。 对于...
m的维度可以自己设定(代码中的hidden_dim),第13行可以看到,messageNN的输入是h_0和h_1做concat就行了,也就是两个节点当前的特征拼接成一个vector作为输入,然后通过linear变换,变成中间向量m,每个邻居节点都会做这个事情然后通过aggr函数得到聚合的m(这个过程和原始的一致)。
CS224W:Graph Representation Learning CS224W:Graph Neural Networks CS224W:Applications of Graph Neural Networks Graph Neural Network (2/2) Refer: 课件:http://web.stanford.edu/class/cs224w/ 视频:https://www.bilibili.com/video/av837826756/ 李宏毅的gnn简介:https://www.youtube.com/watch?v=M9ht8v...
https://www.youtube.com/watch?v=vdNao78TChc&t=933s 【Max Welling】图神经网络知识表示与推荐,Graph Neural Networks for Knowledge Representation and Recommendation 图神经网络资料: https://www.zhuanzhi.ai/topic/2001785662228213 推荐系统: https://www.zhuanzhi.ai/topic/2001106727705358 科技 计算机技术 ...
[2] VideoGraph Convolutional Networks (GCNs) made simple:https://www.youtube.com/watch?v=2KRAOZIULzw [3] PaperSemi-supervised Classification with Graph Convolutional Networks (2017):https://arxiv.org/pdf/1609.02907.pdf [4] GCN source code:https://github.com/tkipf/gcn ...
A Gentle Introduction to Graph Neural Networks https://www.youtube.com/watch?v=GXhBEj1ZtE8 http...
2.2 Graph Neural Networks 对于数据中的高阶结构信息,传统的神经网络很难学习到对应的特征,而GNN的消息传递机制能够很好地整合邻居信息。 三、Challenges of applying GNNs 3.1 Graph Construcion GNN模型主要基于下面的三种图结构: Homogeneous graph:同质图,其中每条边只连接两个节点,并且只有一种类型的节点和边。
Learning phrase representations using RNN encoder-decoder for statistical machine translation EMNLP (2014) P. Covington et al. Deep neural networks for youtube recommendations RecSys (2016) W. Fan et al. Graph neural networks for social recommendation WWW (2019)View more referencesCited by (132) ...
1. 第一代GCN 2. 第二代GCN 3. 切比雪夫GCN 4. 谱图卷积的一阶近似 一、从拉普拉斯算子说起 连...
Covington, P., Adams, J. & Sargin, E. Deep neural networks for youtube recommendations. InProceedings of the 10th ACM Conference on Recommender Systems.https://doi.org/10.1145/2959100.2959190(2016). Luo, S., Lu, X., Wu, J. & Yuan, J. Review-aware neural recommendation with cross-modali...