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Notebook for the practicum: https://github.com/Atcold/pytorch-Deep-Learning/blob/master/16-gated_GCN.ipynb
Introduction to Graph Convolutional Network (GCN): https://atcold.github.io/pytorch-Deep-Learning/en/week13/13-3/
Source: https://youtu.be/2aKXWqkbpWg
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The video was published under the license of the Creative Commons Attribution license (reuse allowed). It is reposted for educational purposes and encourages involvement in the field of research.
In this section, we introduce Graph Convolutional Network (GCN) which is one type of architecture that utilizes the structure of data. Actually, the concept of GCNs is closely related to self-attention. After understanding the general notation, representation and equations of GCN, we delve into the theory and code of a specific type of GCN known as Residual Gated GCN.
0:00:47 – Introduction to Graph Convolutional Network (GCN)
0:16:32 – Residual Gated GCN Theory and Code
0:34:58 – Gated GCNs Implementation Code and Training