图神经网络快速爆发，最新进展都在这里了( 二 )
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论文解析：
5、Knowing Your FATE:Explanations for User Engagement Prediction on Social Apps
Snapchat团队的这篇文章探讨了使用GNNs的社交媒体应用程序中用户的参与度 。 它提出了一个端到端的神经网络框架来预测用户参与度 ， 这些因素包括好友数量和质量、用户发布内容的相关性、用户行为和时间因素 。 这是GNNs最直观的应用之一 。
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论文解析：
下面是CVPR/KDD/ECCV/ICML更多的关于图卷积网络的论文：
[CVPR 2020] Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud[CVPR 2020] Geometrically Principled Connections in Graph Neural Networks [CVPR 2020] SuperGlue: Learning Feature Matching With Graph Neural Networks[CVPR 2020] Learning Multi-View Camera Relocalization With Graph Neural Networks[CVPR 2020] Multi-Modal Graph Neural Network for Joint Reasoning on Vision and Scene Text[CVPR 2020] Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory[CVPR 2020] Dynamic Multiscale Graph Neural Networks for 3D Skeleton Based Human Motion Prediction[CVPR 2020] Dynamic Graph Message Passing Networks[ECCV 2020] Graph convolutional networks for learning with few clean and many noisy labels[ICML 2020] When Spectral Domain Meets Spatial Domain in Graph Neural Networks[KDD 2020] Graph Structural-topic Neural Network[KDD 2020] Towards Deeper Graph Neural Networks[KDD 2020] Redundancy-Free Computation for Graph Neural Networks[KDD 2020] TinyGNN: Learning Efficient Graph Neural Networks[KDD 2020] PolicyGNN: Aggregation Optimization for Graph Neural Networks [KDD 2020] Residual Correlation in Graph Neural Network Regression[KDD 2020] Spotlight: Non-IID Graph Neural Networks[KDD 2020] XGNN: Towards Model-Level Explanations of Graph Neural Networks[KDD 2020] Dynamic Heterogeneous Graph Neural Network for Real-time Event Prediction[KDD 2020] Handling Information Loss of Graph Neural Networks for Session-based Recommendation[KDD 2020] Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks[KDD 2020] GPT-GNN: Generative Pre-Training of Graph Neural Networks[KDD 2020] Graph Structure Learning for Robust Graph Neural Networks[KDD 2020] Minimal Variance Sampling with Provable Guarantees for Fast Training of Graph Neural Networks[KDD 2020] A Framework for Recommending Accurate and Diverse Items Using Bayesian Graph Convolutional Neural Networks[KDD 2020] Competitive Analysis for Points of Interest[KDD 2020] Knowing your FATE: Explanations for User Engagement Prediction on Social Apps[KDD 2020] GHashing: Semantic Graph Hashing for Approximate Similarity Search in Graph Databases
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