Enhanced Graph Convolutional Networks with Adaptive Aggregation

Graph neural networks represent the latest promising advances in deep learning models to analyze graph-structured data, but traditional models, such as the Graph Convolutional Network, have scalability issues such as oversmoothing and lack of generalization. This work extends the standard GCN by proposing adaptive aggregation functions, scalable normalization techniques, and robust regularization methods. The proposed change dynamically selects aggregation strategies based on graph properties, improves the computational efficiency at optimality, and enhances the model generalization, especially on large complex graphs. The improved GCN was tested in relation to the major metrics: accuracy, cross-entropy loss, and F1 score. Experimental results show a 6.5% increase in accuracy and a 20% reduction in runtime compared with baselines like GAT and GraphSAGE. This makes the model more apt for real-time applications and scalable datasets. However, the approach brought out the shortcomings of handling highly dynamic, sparse graphs. Future developments would include joint integration of attention mechanisms with recurrent neural networks, hybrid architectures that capture dynamic graph structures, and better fitting into evolving topologies.