Pure node selection for imbalanced graph node classification | JoVE Visualize

Area of Science:

Graph Neural Networks
Machine Learning
Data Science

Background:

Class imbalance, an uneven distribution of data across classes, is a common issue in machine learning, particularly affecting graph-structured data.
Graph neural networks (GNNs) often assume class balance, leading to performance degradation when faced with imbalanced datasets.
Existing methods struggle to address both quantity and topological imbalance, and a specific problem termed Randomness Anomalous Connectivity Problem (RACP) arises due to random seed sensitivity in GNNs.

Purpose of the Study:

To identify and address the Randomness Anomalous Connectivity Problem (RACP) in graph neural networks (GNNs) caused by random seed sensitivity.
To propose a novel, plug-and-play module that mitigates RACP without requiring specialized algorithms for quantity or topological imbalance.
To enhance the stability and performance of GNNs on imbalanced graph datasets.

Main Methods:

Proposed Pure Node Sampling (PNS), a novel plug-and-play module designed for the node synthesis stage.
PNS operates directly during node synthesis to mitigate RACP and alleviate performance degradation from abnormal neighbor distributions.
Conducted extensive experiments to analyze the influence of random seeds on GNN performance and validate the effectiveness of PNS.

Main Results:

Demonstrated that Pure Node Sampling (PNS) effectively eliminates the performance degradation caused by unfavorable random seeds.
PNS significantly outperforms baseline methods across various benchmark datasets and different GNN backbones.
Experimental results confirm the effectiveness and stability of PNS in handling class imbalance and RACP in graph data.

Conclusions:

Pure Node Sampling (PNS) is an effective and stable solution for addressing the Randomness Anomalous Connectivity Problem (RACP) in GNNs.
PNS offers a versatile, plug-and-play approach to improve GNN performance on imbalanced graph datasets.
The proposed method enhances GNN robustness against random seed variations and abnormal data distributions.