NS - CUK Seminar: S.T.Nguyen, Review on "DropAGG: Robust Graph Neur...

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LAB SEMINAR
Nguyen Thanh Sang
Network Science Lab
Dept. of Artificial Intelligence
The Catholic University of Korea
E-mail...

Content
s
1
⮚ Paper
▪ Introduction
▪ Problem
▪ Contributions
▪ Methodology
▪ Experiments
▪ Conclusion

2
Introduction
Graph Neural Networks (GNNs)
+ GNNs have attracted more and more attention due to its effectiveness on cond...

3
Introduction
Dropout in GNN
+ DropEdge: randomly drops out a certain rate of edges from original edge set.
+ DropNode: r...

4
Problems
+ Existing GNNs generally adopt the deterministic message
propagation mechanism.
 Perform non-robustly w.r.t s...

5
Contributions
• Propose a dropout mechanism DropAGG for GNNs’ message propagation to enhance the
robustness of GNNs and ...

6
Methodology

7
DropAGG
• Randomly sample an indicative variable
• Aggregation function: sum or mean
Bernoulli distribution parameter

8
Graph Random Aggregation Network
• Main idea: use DropAGG.
• Multiple DropAGG branches in GRANet perform the above DropA...

9
Training
• Regularization loss to conduct self-supervised learning in GRANet:
• Focus on semi-supervised learning:
semis...

10
Experiments
Datasets
• Cora.
• Citeseer.
• Polblogs.
• Cora-ML.
• Amazon Photo.

11
Experiments
Baseline comparisons
• Comparing with some recent GNNs including GCN, GAT, APPNP and GMNN, the proposed GRA...

12
Experiments
Robustness results on noisy datasets
• Contrary to some recent GNNs, GRANet generally obtains better perfor...

13
Experiments
Over-smoothing results
• As the propagation step increases, GRANet
maintains better learning performance an...

14
Experiments
Generalization results
• DropAGG can alleviate the over-fitting problem to some extent.

15
Experiments
Ablation study
• DropAGG (DA) mechanism can obviously improve the
learning performance of the baseline meth...

16
Experiments
Experiments on pure DropAGG
• DropAGG mechanism can improve the learning performance of GCN and
GAT.
• Drop...

17
Experiments
Supplementary experiment
• DropEdge GNNs may be unreliable for the very sparse
graph while DropNode may fai...

18
Conclusions
• Propose a novel random message passing mechanism DropAGG based on which we can
derive a robust GNN for gr...

19
Thank you!

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