An Introduction to Graph Neural Networks: Models and Applications
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27.10.22 – 42338:51Опубликовано 8 мая 2020, 18:46
MSR Cambridge, AI Residency Advanced Lecture Series
An Introduction to Graph Neural Networks: Models and Applications
Got it now: "Graph Neural Networks (GNN) are a general class of networks that work over graphs. By representing a problem as a graph — encoding the information of individual elements as nodes and their relationships as edges — GNNs learn to capture patterns within the graph. These networks have been successfully used in applications such as chemistry and program analysis. In this introductory talk, I will do a deep dive in the neural message-passing GNNs, and show how to create a simple GNN implementation. Finally, I will illustrate how GNNs have been used in applications.
microsoft.com/en-us/research/v...
An Introduction to Graph Neural Networks: Models and Applications
Got it now: "Graph Neural Networks (GNN) are a general class of networks that work over graphs. By representing a problem as a graph — encoding the information of individual elements as nodes and their relationships as edges — GNNs learn to capture patterns within the graph. These networks have been successfully used in applications such as chemistry and program analysis. In this introductory talk, I will do a deep dive in the neural message-passing GNNs, and show how to create a simple GNN implementation. Finally, I will illustrate how GNNs have been used in applications.
microsoft.com/en-us/research/v...
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