Weisfeiler and Leman Go Neural: Higher-order Graph Neural Network.pptx

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Ho-Beom Kim
Network Science Lab
Dept. of Mathematics
The Catholic University of Korea
E-mail: hobeom2001@catholic.ac.kr
20...

2
Introduction
Problem Statements
• Graph-structured data is ubiquitous across application domains ranging from chemo- and...

3
Introduction
Contribution
1. We show that GNNs are not more powerful than the 1- WL in terms of distinguishing non-isomo...

4
Methodology
Weisfeiler-Leman Algorithm
K>=2
j-th neighborhood of a k-tuple in

5
Methodology
Weisfeiler-Leman Algorithm
• For iteration t > 0
• WL Kernel
• After running the WL algorithm, the concatena...

6
Methodology
Graph Neural Networks
• In each layer t > 0
Parameter matrices from
A vector representation fGNN over the wh...

7
Methodology
Relationship Between 1-WL and 1-GNNs
Labeled graph
GNN parameters up to iteration t
Encode the initial label...

8
Methodology
K-dimensional Graph Neural Networks
K-set in
Local neighborhood
Global neighborhood =
Coloring
Feature vecto...

9
Methodology
K-dimensional Graph Neural Networks
New features
Local k-GNNs to scale k-GNNs to larger datasets
and global ...

10
Methodology
Illustration of the proposed hierarchical variant of the k-GNN layer.

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Experiments
Datasets
• Benchmark datasets
Baselines
• Kernel Baselines
• Graphlet
• Shortest-path
• Weisfeiler-Lehman s...

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Experiments
Classification accuracies in percent on various graph benchmark datasets.

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Experiments
Mean absolute errors on the Q M 9 dataset.

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Conclusion
Conclusion
• They presented a theoretical investigation of GNNs, showing that a wide class of GNN
architectu...

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