Unlocking the Potential of Message Passing: Exploring GraphSAGE, GCN and GAT | GNN GraphML

Introduction to GRAPH ML, Graph Neural Networks (GNN) and the main idea behind Message Passing in graph network configurations of GraphSAGE, GCN and GAT.
Message passing applied to Graph Convolutional Networks (GCN), GraphSAGE and Graph Attention Networks. The key difference between GAT and GCN is how the information from the k-hop neighborhood is aggregated.
Stanford online: CS224W
• Stanford CS224W: Machi...
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[@vgtgoat]

Thank you I just found your channel and I'm enjoy ing your explanations very much. Many of these concepts I never expected I'd understand as well as I do from watching your videos.

[@code4AI]

You're very welcome!

[@sofiaormazabal6372]

Thank you!! Everything is so clear!!

[@yzz9833]

your dry humor kills me "so the node embedding of node v is h(v), congratulations" 😂

[@code4AI]

Finally someone appreciates my humor! Thank you!

[@-0164-]

Thank you :) Nice explanation

[@code4AI]

Glad it was helpful!

[@jaisingh1292]

Can you create a video on edge classification for heterogeneous graphs

[@code4AI]

Yes, of course. This topic is now in my pipeline, since I'll do a mini-series to code in detail Graph ML topics. Thank you for your comment.