📕Graph Representation Learning

💥Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial if we want systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D-vision, recommender systems, question answering, and social network analysis.

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📄Theory of Graph Neural Networks: Representation and Learning

🗓Publish year: 2022

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📃 Advancing Biomedicine with Graph Representation Learning: Recent Progress, Challenges, and Future Directions

🗓 Publish year: 2023

🧑‍💻Authors: Fang Li, Yi Nian, Zenan Sun, Cui Tao
🏢Universities: the University of Texas Health Science Center at Houston

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