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🎞 Machine Learning with Graphs: Heterogeneous & Knowledge Graph Embedding, Knowledge Graph Completion

💥Free recorded course by Jure Leskovec, Computer Science, PhD

💥 In this lecture, we first introduce the heterogeneous graph with the definition and several examples. In the next, we talk about a model called RGCN which extends the GCN to heterogeneous graph. To make the model more scalable, several approximated approaches are introduced, including block diagonal matrices and basis learning. At last, we show how RGCN predicts the labels of nodes and links.
Then we introduce the knowledge graphs by giving several examples and applications.

📽 Watch: part1 part2

📝 slide

💻 Code

📲Channel: @ComplexNetworkAnalysis

#video #course #Graph #Machine_Learning #Knowledge_Graph #GNN
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Stanford CS224W: ML with Graphs | 2021 | Lecture 10.1-Heterogeneous & Knowledge Graph Embedding
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3pNkBLE

Lecture 10.1 - Heterogeneous Graphs and Knowledge Graph Embeddings
Jure Leskovec
Computer Science, PhD

In this lecture,…
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🎞 Machine Learning with Graphs: Heterogeneous & Knowledge Graph Embedding, Knowledge Graph Completion

💥Free recorded course by Jure Leskovec, Computer Science, PhD

💥 In this lecture, we first introduce the heterogeneous graph with the definition and several examples. In the next, we talk about a model called RGCN which extends the GCN to heterogeneous graph. To make the model more scalable, several approximated approaches are introduced, including block diagonal matrices and basis learning. At last, we show how RGCN predicts the labels of nodes and links.
Then we introduce the knowledge graphs by giving several examples and applications.

📽 Watch: part1 part2

📝 slide

💻 Code

📲Channel: @ComplexNetworkAnalysis

#video #course #Graph #Machine_Learning #Knowledge_Graph #GNN

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🎞 Machine Learning with Graphs: Heterogeneous & Knowledge Graph Embedding

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