Network Analysis Resources & Updates

📄The Essential Guide to GNN (Graph Neural Networks)

💥Technical Paper

💥 Graph neural networks (GNNs) are a set of deep learning methods that work in the graph domain. These networks have recently been applied in multiple areas including; combinatorial optimization, recommender systems, computer vision – just to mention a few. These networks can also be used to model large systems such as social networks, protein-protein interaction networks, knowledge graphs among other research areas. Unlike other data such as images, graph data works in the non-euclidean space. Graph analysis is therefore aimed at node classification, link prediction, and clustering.

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📲Channel: @ComplexNetworkAnalysis

#paper #Graph #code #GNN

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The Essential Guide to GNN (Graph Neural Networks) | Intel® Tiber™ AI Studio

Graph neural networks (GNNs) are a set of deep learning methods that work in the graph domain. These networks have recently been applied in multiple areas

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www.tgoop.com/complexnetworkanalysis/1079

913 viewsedited  Mar 22, 2024 at 19:40

📄The Essential Guide to GNN (Graph Neural Networks)

💥Technical Paper

💥 Graph neural networks (GNNs) are a set of deep learning methods that work in the graph domain. These networks have recently been applied in multiple areas including; combinatorial optimization, recommender systems, computer vision – just to mention a few. These networks can also be used to model large systems such as social networks, protein-protein interaction networks, knowledge graphs among other research areas. Unlike other data such as images, graph data works in the non-euclidean space. Graph analysis is therefore aimed at node classification, link prediction, and clustering.

🌐 Study

📲Channel: @ComplexNetworkAnalysis

#paper #Graph #code #GNN

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