The 2nd NS-CUK Weekly Seminar Presenter: Van Thuy Hoang Date: Mar 6th, 2023 Topic: Review on "Everything is Connected: Graph Neural Networks," Current Opinion in Structural Biology Schedule: https://nslab-cuk.github.io/seminar/

1. Hoang Van Thuy
Network Science Lab
E-mail: hoangvanthuy90@gmail.com

2. 1
• GFF, graph neural networks are trained
greedily layer by layer, using both positive
and negative samples
• Solve the noise from neighbours

3. 2
The fundamentals: Permutation equivariance and invariance
➢ node feature matrix.
➢ adjacency matrix, A
➢ Neighbours of nodes
➢ Features
➢ Hidden state

4. 3
Graph Neural Networks
➢ Main Idea: Pass massages between pairs of nodes and agglomerate
➢ Alternative Interpretation: Pass massages between nodes to refine node
(and possibly edge) representations

5. 4
Graph Neural Networks
➢ Convolutional

6. 5
Graph Neural Networks
➢ Attentional

7. 6
Graph Neural Networks
➢ Message-passing

8. 7
Graph Neural Networks
➢ Graph Convolutional Networks (GCNs)
Kipf & Welling (ICLR 2017), related previous works by D
uvenaud et al. (NIPS 2015) and Li et al. (ICLR 2016)

9. 8
Tasks
➢ Node classification.
➢ Graph classification.
➢ Link prediction

10. 9
GNNs without a graph: Deep Sets and Transformers
➢ Deep Sets

11. 10
GNNs without a graph: Deep Sets and Transformers
➢ To reverse-engineer why Transformers appear here,
let us consider the NLP perspective.

12. 11
GNNs beyond permutation equivariance: Geometric Graphs
➢ We have assumed our graphs to be a discrete, unordered, collection of
nodes and edges—hence, only susceptible to permutation symmetries.
➢ But in many cases, this is not the entire story!

13. 12