Research on Feature Extraction Algorithm of Graph Data Based on Graph Neural Network

This paper proposes an optimization model that integrates Hadoop, Graph Convolutional Networks (GCN), and Ant Colony Optimization (ACO) to enhance GNN performance in large-scale networks. Hadoop is utilized for distributed storage and processing of large graph datasets, dividing them into subgraphs across multiple computing nodes. The MapReduce framework accelerates the training and inference of GCN through parallel computing. GCN is employed to extract node features by performing convolution on the graph's adjacency matrix. ACO is then applied for global hyperparameter tuning and network structure optimization. By exploring the parameter space, ACO identifies the optimal configuration for GCN. This model leverages Hadoop's distributed computing power, GCN's effective feature extraction, and ACO's optimization capabilities. Experimental results demonstrate improved efficiency and performance on large-scale network datasets compared to traditional methods.