Imbalanced node classification with Graph Neural Networks: A unified approach leveraging homophily and label information

1. Van Thuy Hoang
Network Science Lab
Dept. of Artificial Intelligence
The Catholic University of Korea
E-mail: hoangvanthuy90@gmail.com
2023-11-13
Dingyang Lv et. al.; A.S.C 23

2. 2
Graph Convolutional Networks (GCNs)
 Generate node embeddings based on local network neighborhoods
 Nodes have embeddings at each layer, repeating combine messages
from their neighbor using neural networks

3. 3
phenomenon
 Many real-world graphs: as neighbor class imbalance, which is characterized by
frequent connections between dissimilar nodes, a scenario reflecting low
homophily
 Classical GNNs tend to overlook this issue, leading to a significant decline in
performance.

4. 4
phenomenon
 the key issues of Neighbor Class Imbalance: Supposing target node 𝑣 is a fraud
object, its neighbors in the dashed circle are class imbalanced since benign
nodes are significantly more than fraud ones
Class-imbalanced Learning

5. 5
Class-imbalanced Learning
 variable homophily degrees

6. 6
Methodology
 the proposed HLA-GNN

7. 7
Methodology
 Homophily degree estimation
HLA-GNN can calculate the label similarity between any
pair of nodes:

8. 8
Methodology
 Label propagation enhanced representation
where 𝑌 (0) is the initial node label matrix
the predicted label of the unlabeled node u:

9. 9
Methodology
 The algorithm details of HLA-GNN for node label and feature fusion
propagation

10. 10
Methodology
 High-order message aggregation

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Experiments
 HLA-GNN is evaluated on 8 datasets

12. 12
Experiments
 Node classification tasks on 8 datasets

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Conclusion
 a novel message passing based on homophily degree estimation and label
propagation to enhance the quality of representation learning for the classic
GNNs.
 three key techniques:
 a homophily degree estimation
 label propagation enhanced representation
 high-order neighborhood information aggregation