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Daily Dose of Data Science | Avi Chawla | Substack

Discover essential techniques for graph feature engineering, crucial for building effective graph neural networks (GNNs). Learn how to create a dummy social networking graph dataset and derive key features like node degree and centrality measures using NetworkX. The post highlights the significance of these features in enhancing model performance and provides real-world examples of graph machine learning applications by tech giants. Gain insights into various GNN tasks, data challenges, frameworks, and advanced architectures.

6 Graph Feature Engineering Techniques

P.S. For those wanting to develop “Industry ML” expertise: