GraphSR: A Data Augmentation Algorithm for Imbalanced Node Classification

02/24/2023
by   Mengting Zhou, et al.
0

Graph neural networks (GNNs) have achieved great success in node classification tasks. However, existing GNNs naturally bias towards the majority classes with more labelled data and ignore those minority classes with relatively few labelled ones. The traditional techniques often resort over-sampling methods, but they may cause overfitting problem. More recently, some works propose to synthesize additional nodes for minority classes from the labelled nodes, however, there is no any guarantee if those generated nodes really stand for the corresponding minority classes. In fact, improperly synthesized nodes may result in insufficient generalization of the algorithm. To resolve the problem, in this paper we seek to automatically augment the minority classes from the massive unlabelled nodes of the graph. Specifically, we propose GraphSR, a novel self-training strategy to augment the minority classes with significant diversity of unlabelled nodes, which is based on a Similarity-based selection module and a Reinforcement Learning(RL) selection module. The first module finds a subset of unlabelled nodes which are most similar to those labelled minority nodes, and the second one further determines the representative and reliable nodes from the subset via RL technique. Furthermore, the RL-based module can adaptively determine the sampling scale according to current training data. This strategy is general and can be easily combined with different GNNs models. Our experiments demonstrate the proposed approach outperforms the state-of-the-art baselines on various class-imbalanced datasets.

READ FULL TEXT
research
06/10/2022

Synthetic Over-sampling for Imbalanced Node Classification with Graph Neural Networks

In recent years, graph neural networks (GNNs) have achieved state-of-the...
research
11/29/2022

Every Node Counts: Improving the Training of Graph Neural Networks on Node Classification

Graph Neural Networks (GNNs) are prominent in handling sparse and unstru...
research
10/22/2021

Distance-wise Prototypical Graph Neural Network in Node Imbalance Classification

Recent years have witnessed the significant success of applying graph ne...
research
03/16/2021

GraphSMOTE: Imbalanced Node Classification on Graphs with Graph Neural Networks

Node classification is an important research topic in graph learning. Gr...
research
06/15/2022

CLNode: Curriculum Learning for Node Classification

Node classification is a fundamental graph-based task that aims to predi...
research
10/30/2020

Deep Active Graph Representation Learning

Graph neural networks (GNNs) aim to learn graph representations that pre...

Please sign up or login with your details

Forgot password? Click here to reset