AG-GNN: Adaptive gating mechanism for robust node classification in graph neural networks

Graph Neural Networks (GNNs) have revolutionized node classification tasks by leveraging graph structure and node features through message-passing schemes. However, GNNs frequently suffer from over-smoothing as the number of layers increases, causing node representations to collapse and lose discriminative power. In this paper, we propose AG-GNN, a novel architecture that addresses these challenges through a simple yet effective adaptive gating mechanism. This mechanism acts as a smart switch that dynamically controls how much information flows from the graph structure versus the node features at each layer. Our model’s dual-pathway design enables it to excel in both homophilic graphs (where connected nodes tend to share the same class) and heterophilic graphs (where connected nodes often belong to different classes). Extensive experiments demonstrate that AG-GNN consistently outperforms state-of-the-art methods, achieving up to 2.16 % improvement on heterophilic datasets like Cornell and 5.86 % improvement on large-scale networks like Penn94. Importantly, our approach maintains strong performance even with very deep architectures (up to 64 layers), demonstrating remarkable resistance to over-smoothing where traditional GNNs fail. AG-GNN scales efficiently to graphs with millions of nodes while maintaining computational tractability where several baseline models experience out-of-memory errors. • Our model dynamically balances node features and graph structure through an adaptive gating mechanism. • A novel multi-pathway architecture automatically handles both homophilic and heterophilic graphs. • The proposed model maintains stable performance across network depths without over-smoothing. • Extensive experiments on 15 datasets demonstrate superior performance and scalability. • The model effectively scales to graphs with millions of nodes where baseline models fail.