【KDD2022 图神经网络主题论文】#值得收藏的论文合集#
1.Feature Overcorrelation in Deep Graph Neural Networks: A New Perspective
2.Pre-training Enhanced Spatial-temporal Graph Neural Network for Multivariate Time Series Forecasting
3.CoRGi: Content-Rich Graph Neural Networks with Attention
4.Instant Graph Neural Networks for Dynamic Graphs
5.Motif Prediction with Graph Neural Networks
6.GPPT: Graph Pre-training and Prompt Tuning to Generalize Graph Neural Networks
7.Graph Neural Networks with Node-wise Architecture
8.Streaming Graph Neural Networks with Generative Replay
9.Dynamic Graph Segmentation for Deep Graph Neural Networks
10.On Structural Explanation of Bias in Graph Neural Networks
11.Compressing Deep Graph Neural Networks via Adversarial Knowledge Distillation
12.Improving Social Network Embedding via New Second-Order Continuous Graph Neural Networks
13.GUIDE: Group Equality Informed Individual Fairness in Graph Neural Networks
14.Connecting the Hosts: Street-Level IP Geolocation with Graph Neural Networks
15.Graph2Route: A Dynamic Spatial-Temporal Graph Neural Network for Pick-up and Delivery Route Prediction

论文合集：https://t.cn/A6SjaHlO

Graph Neural Networks（GNN）在近几年的研究和应用中可以说是炙手可热。每年以Graph为名的论文数呈现井喷态势。而在最近的KDD大会上，目测有三分之二左右的大会内容和图或GNN有直接或者间接的联系。然而要想在繁多的论文中理出头绪，对于初学者和普通工程师而言，是一件非常具有挑战的事情。

在KDD 2022大会上Lingfei Wu等多位学者做了一个关于GNN的讲座名为“Graph Neural Networks (GNNs): Foundation, Frontiers and Applications”。这个讲座可以说是涵盖了GNN方面的绝大多数初学内容，包括理论和实践，以及好几个方面的应用。这个讲座还有更加丰富对应的书籍可供大家深入阅读。

#田村保乃[超话]# #田村保乃#
via. BLT官推

预约开始
blt graph.vol.83
封面是 #櫻坂46#　#田村保乃#

远离平日的喧嚣
在美丽大自然的包围中
自1年半以前的「blt graph.」登场后进一步成长了的田村
展现出了艳丽的女性美
慵懒的表情
以及一直未曾改变的温柔笑颜

附带海报 9月20日发售

LAWSON(※仅限WEB)更有特典海报！