前面是GE领域的概述了,现在要说的是另一个事情就是图卷积(Graph Convolutional Neural Network,GCN),其实这相对于GE是另一个思路,GE基于高维相似性映射到低维以后也是相似的,我们想使用深度学习应该先学习图嵌入(借鉴nlp中的word2vec) ,而GCN就是直接端到端分类或回归,当然也可以先使用进行图嵌入,拿到嵌入向量以后...
Neural networks are often represented as graphs of connections between neurons. However, despite their wide use, there is currently little understanding of the relationship between the graph structure of the neural network and its predictive performance. Here we systematically investigate how does the gr...
Neural networks are often represented as graphs of connections between neurons. However, despite their wide use, there is currently little understanding of the relationship between the graph structure of the neural network and its predictive performance. Here we systematically investigate how does the gr...
目前“paper界” 更加关注的是,GNN model如何更好地利用和表达graph本身的拓扑结构信息的能力。 我一开始看这方面的文献云里雾里的,一直在考虑node/edge/graph features的问题,但是其实,这里的研究更加偏向于纯粹的拓扑结构信息利用:即,很多论文的基本假设就是node/edge/graph是不存在features(也可以认为features是完全...
with them. In recent years, systems based on variants of graph neural networks such as graph convolutional network (GCN), graph attention network (GAT), gated graph neural network (GGNN) have demonstrated ground-breaking performance on many tasks mentioned above. In this survey, we provide a ...
前面是GE领域的概述了,现在要说的是另一个事情就是图卷积(Graph Convolutional Neural Network,GCN),其实这相对于GE是另一个思路,GE基于高维相似性映射到低维以后也是相似的,我们想使用深度学习应该先学习图嵌入(借鉴nlp中的word2vec) ,而GCN就是直接端到端分类或回归,当然也可以先使用进行图嵌入,拿到嵌入向量以后...
We start by revisiting the definition of a neural network from the graph perspective. We define a graph G = (V, E) by its node set V = {v1, ..., vn} and edge set E ⊆ {(vi , vj )|vi , vj ∈ V}. We assume each node v has a node feature scalar/vector xv. ...
由于应用很广泛(主要是社交网络发展和知识图谱的推动),以及受到深度学习在其他领域成功的启示,这个方向是目前机器学习领域最火的方向之一了,KDD2018中31篇tutorials里面有9篇是关于graph的,bestpaper也是关于graph的,论文名字叫做:adversarial attacks onclassification models for graphs. 可见学术界和工业界的热情。
在2.3 节中,文章介绍了近年来文献中提出的图神经网络通用框架 MPNN(Message Passing Neural Network)、NLNN(Non-local Neural Network)以及 Deepmind 的 GN(Graph Network)。 MPNN 将模型总结为信息传递阶段和节点更新阶段,概括了多种图神经网络和图卷积神经网络方法。NLNN 总结了很多种基于自注意力机制的方法。GN 提...
而就 2020的情况来看,这个趋势还在不断扩大。总之,Graph Neural Network (简称“GNN”)在2019- 2020年之间,力压 Deep Learning、GAN等,成为各大顶会的增长热词,且GNN在各个领域越来越受到欢迎,包括社交网络、知识图谱、推荐系统,甚...