Graph Machine Learning Overview: Traditional ML to Graph Neural Networks
Explore the evolution of Machine Learning in Graphs, from traditional ML tasks to advanced Graph Neural Networks (GNNs). Discover key concepts like feature engineering, tools like PyG, and types of ML tasks in graphs. Uncover insights into node-level, graph-level, and community-level predictions, an
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Graph Neural Networks
Graph Neural Networks (GNNs) are a versatile form of neural networks that encompass various network architectures like NNs, CNNs, and RNNs, as well as unsupervised learning models such as RBM and DBNs. They find applications in diverse fields such as object detection, machine translation, and drug d
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Enhancing Graph Neural Networks for Text-rich Graphs
Designing GNNs for text-rich graphs involves considering both textual and non-textual linkage information among entities, such as papers, webpages, and people. Utilizing structural information beyond citation networks and exploring latent textual linkages are key challenges. Previous work has focuse
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