Network Analysis Resources & Updates

🎞 Machine Learning with Graphs: Pre-Training Graph Neural Networks

💥Free recorded course by Prof. Jure Leskovec

💥There are two challenges in applying GNNs to scientific domains: scarcity of labeled data and out-of-distribution prediction. In this video we discuss methods for pre-training GNNs to resolve these challenges. The key idea is to pre-train both node and graph embeddings, which leads to significant performance gains on downstream tasks.

📽 Watch
📑More details can be found in the paper: Strategies for Pre-training Graph Neural Networks

📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #GNN #Machine_Learning

arXiv.org

Strategies for Pre-training Graph Neural Networks

Many applications of machine learning require a model to make accurate pre-dictions on test examples that are distributionally different from training ones, while task-specific labels are scarce...

www.tgoop.com/complexnetworkanalysis/1120

993 viewsedited  Apr 30, 2024 at 19:32

🎞 Machine Learning with Graphs: Pre-Training Graph Neural Networks

💥Free recorded course by Prof. Jure Leskovec

💥There are two challenges in applying GNNs to scientific domains: scarcity of labeled data and out-of-distribution prediction. In this video we discuss methods for pre-training GNNs to resolve these challenges. The key idea is to pre-train both node and graph embeddings, which leads to significant performance gains on downstream tasks.

📽 Watch
📑More details can be found in the paper: Strategies for Pre-training Graph Neural Networks

📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #GNN #Machine_Learning

BY Network Analysis Resources & Updates