A Hybrid Deep Learning Approach for Influential Nodes Identification in Complex Networks
TL;DR
CNNG, a novel hybrid deep learning model combining Convolutional Neural Networks and Graph Attention Networks for predicting node influence, is proposed, which achieves the best average Kendall’s Tau coefficient and the highest average Monotonicity Index over ten networks.
Abstract
The influential nodes in complex network are the key of high effective information spreading. Several techniques have been developed for the discovery of such nodes, including centrality-based approaches, machine learning-based approaches, and deep learning-based approaches. This paper proposes CNNG, a novel hybrid deep learning model combining Convolutional Neural Networks (CNN) and Graph Attention Networks (GAT) for predicting node influence. CNNG processes each node’s local subgraph through parallel CNN and GAT components to capture both structural patterns and relational dependencies. The model is trained in a supervised regression setting, using node influence scores generated via SIR simulations. Experiments on twelve real networks show that CNNG achieves the best average Kendall’s Tau coefficient of 0.7510, outperforming the second-best method by 2.85% as well as the highest average Monotonicity Index of 0.9954 and the highest average Jaccard Similarity of 0.7298 over ten networks. Further, CNNG provides these results in a reasonable amount of computing time, which demonstrates the practicability, efficiency and generality of CNNG for the complex network analysis.