Identifying the Diffusion Source Based on Graph Neural Networks and Node Structural Features

Accurately identifying the diffusion source is an effective control strategy in complex network diffusion processes. Current methods predominantly focus on temporal and directional characteristics of observed nodes, yet frequently overlook structural properties and second-order neighborhood influences. To address this, we propose an Integrated Dual-Mapping Twin-Graph Neural Network (IDM-TGN) framework for source identification. IDM-TGN consists of two core modules: Integrated Structural Dual-Mapping (ISDM) module and Twin-GNN Fusion Architecture (TG-FA). The ISDM module leverages the structural holes centered on observation nodes to extract and integrate multiple distinct diffusion features into a unified feature matrix. The TG-FA module employs two distinct graph neural networks to directly identify the diffusion source from the input feature matrix. Extensive experiments on diverse synthetic and real networks demonstrate the effectiveness and feasibility of IDM-TGN for source identification.