Kdd 2023 - Universal And Generalizable Structure Learning For Graph Neural Networks

KDD 2023 - Universal and Generalizable Structure Learning for Graph Neural Networks

Association for Computing Machinery (ACM)

KDD 2023 - Graph Structure Learning via Progressive Strategy

Association for Computing Machinery (ACM)

KDD 2023 - Learning Strong Graph Neural Networks with Weak Information

Association for Computing Machinery (ACM)

KDD 2023 - Generalizing Explanations for Graph Neural Networks with Data Augmentation

Association for Computing Machinery (ACM)

KDD 2020: Lecture Style Tutorials: Deep Graph Learning Foundations, Advances and Applications

Association for Computing Machinery (ACM)

KDD 2023 - Discovering Dynamic Causal Space for DAG Structure Learning

Association for Computing Machinery (ACM)

KDD 2023 - Hyperbolic Graph Neural Networks: A Tutorial on Methods and Applications

Association for Computing Machinery (ACM)

KDD 2020: Hands-on Tutorials: Scalable Graph Neural Networks with Deep Graph Library

Association for Computing Machinery (ACM)

Stanford CS224W: Machine Learning w/ Graphs I 2023 I Graph Neural Networks

Stanford Online

KDD 2023 - Graphs, Databases and Machine Learning

Association for Computing Machinery (ACM)

KDD 2023 - Transferable Graph Structure Learning for Graph-based Traffic Forecasting Across Cities

Association for Computing Machinery (ACM)

KDD 2023 - On the Predictive Power of Graph Neural Networks

Association for Computing Machinery (ACM)

KDD 2023 - Multi-Task Prompting for Graph Neural Networks

Association for Computing Machinery (ACM)

KDD 2020: Hands On Tutorials: Neural Structured Learning-Training neural networks

Association for Computing Machinery (ACM)

KDD 2023 - Clenshaw Graph Neural Networks

Association for Computing Machinery (ACM)

KDD 2023 - Certified Edge Unlearning for Graph Neural Networks

Association for Computing Machinery (ACM)

KDD 2023 - Enhancing Graph Representations Learning with Decorrelated Propagation

Association for Computing Machinery (ACM)