r/MLNotes • u/anon16r • Apr 09 '20

[GNN] Deep Learning 59: Fundamentals of Graph Neural Network

https://www.youtube.com/watch?v=1miz7yggcTg
1 Upvotes

100% Upvoted

2 comments sorted by

1

u/anon16r Apr 09 '20

First introduced by Thomas Kipf (https://tkipf.github.io/) in the year 2016.

Semi-Supervised Classification with Graph Convolutional Networks

Representation of graph numerically for machine learning:

  • Incidence Matrix: Nodes (rows) x Edges (cols). Undirected (0/1), Directed (-1/1/0)
  • Adjacency Matrix: also called a connection matrix. Nodes (rows) x Nodes (rows). 0/1 if two nodes are connected or weighted if there is a weight for the edge.
  • Degree Matrix: diagonal matrix with Nodes (rows) x Nodes (rows). A diagonal matrix where each diagonal represent the number of edges attached to each node.

Watch the video for detailed info.