今天带来的是一篇经典论文《Line Graph Neural Networks for Link Prediction》,提出线图在链路预测上的应用。论文发表在TPAMI上,由微软的Lei Cai等人完成。论文在Google Scholar上有200+的引用量,成为事实上的链路预测SOTA模型。 论文概况 论文发表时间是2021年,实际上2020年就投到arXiv上了,从时间上来说,是相当领先...
Line Graph Neural Networks for Link Weight Prediction 下载积分:199 内容提示: 文档格式:PDF | 页数:10 | 浏览次数:1 | 上传日期:2024-11-11 07:05:45 | 文档星级: 阅读了该文档的用户还阅读了这些文档 8 p. Tensegrity Robot Proprioceptive State Estimation with Geometric Constraints 14 p. ...
根据时序边之间的时间间隔设置该line graph中的边权重,发生在时间上更接近的时序边具有更高的权重。从这个派生的line graph中,可以使用高效的传统方法计算原始网络的时序边表示,这个line graph直接表示拓扑接近度(即边的邻接性)和时间接近度,时序边的表示可以用于有效的经典方法。此外,这篇论文提出了提出了时间随机块...
2. 网络表示学习(Network Representation Learning,NRL),也称为图嵌入法(Graph Embedding Method,GEM):用低维、稠密、实值的向量表示网络中的节点(含有语义关系,利于计算存储,不用再手动提特征(自适应性),且可以将异质信息投影到同一个低维空间中方便进行下游计算)。 DeepWalk【1】: 实现1:https://github.com/...
It is well known that a shaded link diagram corresponds to a signed plane multi-graph. In graph theory, line graph is an old and important concept originally introduced by H. Whitney in 1932. In this paper we define the line graph link to be a link which has a diagram whose correspondin...
Graph neural networks (GNN) have been shown to provide substantial performance improvements for atomistic material representation and modeling compared with descriptor-based machine learning models. While most existing GNN models for atomistic prediction
This paper studies the problem of embedding very large information networks into low-dimensional vector spaces, which is useful in many tasks such as visualization, node classification, and link prediction. Most existing graph embedding methods do not scale for real world information networks which usu...
Such a low-dimensional embed- ding is very useful in a variety of applications such as vi- sualization [21], node classification [3], link prediction [10], and recommendation [23]. Various methods of graph embedding have been proposed in the machine learning literature (e.g., [4, 20,...
伪代码 [Graph Embedding] node2vec 在前面两篇文章中,我们分别介绍了DeepWalk 和 LINE,其中DeepWalk使用随机游走来扩展节点的领域。本文的node2vec的中心思想和DeepWa...Node2Vec论文学习笔记——1 网络中的链路预测(Link Prediction)是指如何通过已知的网络节点以及网络结构等信息预测网络中尚未产生连边的两个节点...
Through this work, we demonstrate the utility of graph-based featurization and modeling methods in the prediction of complex targets that depend on both chemistry and directional characteristics of material structures.doi:10.1007/s11837-022-05199-yKaundinya Prathik R....