This document introduces a novel framework called Graph Pointer Neural Networks (GPNN) for learning node representations in heterophilic graphs. GPNN incorporates a graph pointer generator into the GNN architecture to distinguish crucial information from distant nodes and perform non-local aggregation selectively. Experimental results show that GPNN outperforms previous state-of-the-art methods, achieving an average lift of 6.3% on node classification tasks. The document analyzes GPNN's performance on heterophilic graphs and its ability to mitigate over-smoothing issues.

1. Joo-Ho Lee
School of Computer Science and Information Engineering,
The Catholic University of Korea
E-mail: jooho414@gmail.com
2023-07-03

2. 1
Introduction
Problem Statement
• The aggregation often needs to be permutation-invariant
• However, this inductive bias is not suitable for graphs with heterophily
• In heterophilic graphs, most connected nodes are dissimilar and may belong to different classes, while the
semantically relevant nodes are often multi-hops away

3. 2
Introduction
Problem Statement
• Few recent studies address heterophilic graphs by combining multiple hops of hidden representations of
central nodes or ranking the nodes with attentions scores
• The limitations of these methods lie in two aspects
1. multi-hop based approaches do not explicitly distinguish relevant nodes from a large number of multi
hop neighborhoods
2. the ranking procedure in state-of-the-art solution is non-differentiable

4. 3
Introduction
Contribution
• They propose a novel framework termed Graph Pointer Neural Networks (GPNN) tailored for heterophilic
graphs
• Experimental results show that GPNN consistently outperforms previous methods, with an average lift of
6.3% over the best state-of-the-art method
• They further demonstrate the privilege of GPNN through extensive analysis

5. 4
Method
Architecture

6. 5
Method
Graph Pointer Generator

7. 6
Method
Graph Pointer Generator

8. 7
Experiment
Datasets

9. 8
Experiment
Node classification

10. 9
Experiment
Comparision of homophily ratios between 1-hop neighbors

11. 10
Experiment
Over-Smoothing

12. 11
Conclusion
• In this work, they focus on the node representation learning of heterophilic graphs and present a novel GNN
framework termed Graph Pointer Neural Networks (GPNN)
• They propose to incorporate a graph pointer generator to the GNN architecture, which distinguishes crucial
information from distant nodes and performs non-local aggregation selectively
• Experiments demonstrate the superiority of GPNN over previous state-of-the-art GNN models