Imbalanced Node Classification in Graphs using SMOTE with GNNs

Mohd Wajahath Ullah

 PG Schlar, Dept of CSE

Sphoorthy Engineering College Hyderabad, India mohammedwajahath444@gmail.com.

Dr. Kaja Masthan

Assistant Professor, Dept of CSE Sphoorthy Engineering College Hyderabad, India

Dr. Kiran B M

HOD, Dept of CSE

Sphoorthy Engineering College Hyderabad, India

ABSTRACT
Node classification is an important research topic in graph learning. Graph neural networks (GNNs) have achieved state-of-the-art performance of node classification. However, existing GNNs address the problem where node samples for different classes are balanced; while for many real-world scenarios, some classes may have much fewer instances than others. Directly training a GNN classifier in this case would under-represent samples from those minority classes and result in sub-optimal performance. Therefore, it is very important to develop GNNs for imbalanced node classification. However, the work on this is rather limited. Hence, we seek to extend previous imbalanced learning techniques for i.i.d data to the imbalanced node classification task to facilitate GNN classifiers. In particular, we choose to adopt synthetic minority oversampling algorithms, as they are found to be the most effective and stable. This task is non-trivial, as previous synthetic minority oversampling algorithms fail to provide relation information for newly synthesized samples, which is vital for learning on graphs. Moreover, node attributes are high-dimensional. Directly over-sampling in the original input domain could generates out-of-domain samples, which may impair the accuracy of the classifier. We propose a novel framework, GraphBalance, in which an embedding space is constructed to encode the similarity among the nodes. New samples are synthesize in this space to assure genuineness. In addition, an edge generator is trained simultaneously to model the relation information, and provide it for those new samples. This framework is general and can be easily extended into different variations. The proposed framework is evaluated using three different datasets, and it outperforms all baselines with a large margin.