2-1 复杂图上的机器学习研究.pdf

1、DGL与复杂图上的机器学习甘全 亚马逊云科技|01DGL与异质图与异质图02DGL与动态图与动态图03DGL与超图与超图目录目录 CONTENT|00图与图神经网络图与图神经网络图与图神经网络图与图神经网络00|图的应用|图神经网络主流是消息传递消息传递，或者其变种端到端训练（绝大部分）或作为预处理的一部分（SGC、SIGN、GAMLP 2）也有使用Transformer架构的（Graphormer 4）1 Geometric Deep Learning Grids,Groups,Graphs,Geodesics,and Gauges,Bronstein et al.,20212 Graph

2、Attention MLP with Reliable Label Utilization,Zhang et al.,20213 Graph Neural Networks Inspired by Classical Iterative Algorithms,Yang et al.,20214 Do Transformers Really Perform Bad for Graph Representation?Ying et al.,2021消息传递结构的数学依据从谱分析的表达式归纳得到（GCN 1）对图上某一类特定的损失函数的优化过程可以视为消息传递（TWIRLS 2）在图上进行热力学扩散

3、的过程可以认为是消息传递（PageRank,APPNP 3,GRAND 4）1 Semi-Supervised Classification with Graph Convolutional Networks,Kipf&Welling,20162 Graph Neural Networks Inspired by Classical Iterative Algorithms,Yang et al.,20213 Predict then Propagate:Graph Neural Networks meet Personalized PageRank,Gasteiger et al.,2018

4、4 GRAND:Graph Neural Diffusion,Chamberlain et al.,2021DGL与异质图与异质图01|为什么需要异质图（Heterogeneous Graph）？|1 A Survey of Heterogeneous Information Network Analysis,Shi et al.,20152 https:/ 1 介绍|包括25个模型以及20+个数据集（包括所有HGB2的节点分类与链路预测数据集）1 https:/ Are we really making much progress?Revisiting,benchmarking,and re

5、fining heterogeneous graph neural networks,Lv et al.,2021从同质图模型到异质图模型的演化|核心是如何利用点边类型绝大部分：每一种边类型准备一种函数或一套参数GraphSAGE RGCN 1,GAT RGAT,HGT 2抽取数个仅包括部分边类型的子图，然后组合SIGN NARS 3以元路径（metapath）构造新图，将异质图同质化GAT HAN 41 Modeling Relational Data with Graph Convolutional Networks,Schlichtkrullet al.,20172 Heterogene

6、ous Graph Transformer,Hu et al.,20203 Scalable Graph Neural Networks for Heterogeneous Graphs,Yu et al.,20204 Heterogeneous Graph Attention Network,Wang et al.,2019从同质图模型到异质图模型的演化|从同质图模型设计空间 1 到异质图模型设计空间 2 的演化|1 Design Space for Graph Neural Networks,You et al.,20212 Space4HGNN:A Novel,Modularized a