Addressing imbalance in graph datasets: Introducing GATE-GNN with graph ensemble weight attention and transfer learning for enhanced node classification

Abdul Joseph Fofanah, David Chen, Lian Wen, Shaoyang Zhang

Abstract: Highlights•We propose a GATE-GNN model for imbalanced node classification with imbalanced datasets.•We design a dynamic node interaction within GATE architecture and leveraging learnable weights.•We proposed GEWA as self-attention mechanism to enhance GNFE and NETL feature representation.•The proposed model showcased robust performance across four GNN imbalanced datasets.