文章arxiv地址:Do Transformers Really Perform Bad for Graph Representation? 简介 本文提出了一种方法Graphormer,Graphormer建立在标准的Transformer结构上,但是在一系列的图表示学习任务上取得了不错的性能。作者人为其模型成功的主要因素为在Transformer的结构中有效利用图的结构信息。 图一Graphormer结构示意图 GNN和Tran...
GitHub - microsoft/Graphormer: This is the official implementation for "Do Transformers Really Perform Bad for Graph Representation?".github.com/microsoft/Graphormer 首先介绍下这个比赛,它有三个图任务: 第一个是学术paper的图,来预测节点的subject area,是一个节点级别的任务; 第二个是knowledge graph,...
Yet, it has not achieved competitive performance on popular leaderboards of graph-level prediction compared to mainstream GNN variants. Therefore, it remains a mystery how Transformers could perform well for graph representation learning. In this paper, we solve this mystery by pres...
该论文Do Transformers Really Perform Bad for Graph Representation探索了使用Transformer Encoder进行图神经网络的编码时,一些可以用到的技巧,包括: 在节点本身的特征基础上,添加入度和出度特征,论文称之为集中度编码(Centrality Encoding) 为了添加空间信息,计算每个节点和其他节点的attention score时,加入两节点最短路径...
论文阅读《Do Transformers Really Perform Bad for Graph Representation?》,融合graph中的特征信息到Transformer中以获取足够表达能力的结构。
论文阅读《Do Transformers Really Perform Bad for Graph Representation?》,程序员大本营,技术文章内容聚合第一站。
論文標題:Do Transformers Really Perform Bad for Graph Representation? 論文作者:Chengxuan Ying, Tianle Cai, Shengjie Luo, Shuxin Zheng, Guolin Ke, Di He, Yanming Shen, Tie-Yan Liu 論文來源:2021, NeurIPS 論文地址:download 論文程式碼:download ...
and Liu T. Do transformers really perform badly for graph representation? NIPS, 2021.概本文提出了一种基于图的 Transformer 架构, 其中 centrality, spatial 和 edge encoding 经过了特殊的设计.符号说明G=(V,E)G=(V,E),图; V={v1,v2,…,vn}V={v1,v2,…,vn}; xixi, node featuresl h(l)...
Yet, it has not achieved competitive performance on popular leaderboards of graph-level prediction compared to mainstream GNN variants. Therefore, it remains a mystery how Transformers could perform well for graph representation learning. In this paper, we solve this mystery by presenting Graphormer, ...
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