🛣[Deep Learning]Stanford CS224w:Machine Learning with Graphs
想说的话🎇
🔝课程网站：http://web.stanford.edu/class/cs224w/
👀一些资源: B站精讲：https://www.bilibili.com/video/BV1pR4y1S7GA/?spm_id_from=333.337.search-card.all.click&vd_source=280e4970f2995a05fdeab972a42bfdd0
https://github.com/TommyZihao/zihao_course/tree/main/CS224W
Slides: http://web.stanford.edu/class/cs224w/slides
Geometric Graphs
A geometric graph \(G=(A,S,X)\) is a graph where each node is embeddedd in \(d\)-dimensional Euclidean space:
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\(A\): an \(n \times n\) adjacency matrix
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\(S \in \mathbb{R}^{n \times f}\): scalar features
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\(X \in \mathbb{R}^{n \times d}\): tensor features(e.g.,coordinates)
Geometric GNNs
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Invariant GNNs for learning invariant scalar features
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Equivariant GNNs for learning equivariant tensor features