$PathSAGE$: Identifying Influential Spreaders in Temporal Networks With $GraphSAGE$

Identifying influential spreaders in temporal networks is crucial for understanding and controlling the dynamics of spreading. However, existing methods, such as temporal betweenness, closeness, pagerank, degree, and local path-based centrality, face several limitations, including high computational complexity, reliance on shortest paths, convergence issues, inability to capture influence dynamics with insufficient neighboring nodes, and a primary focus on local structural information. This paper presents$PathSAGE$, a novel method that addresses these problems. It integrates$GraphSAGE$, a deep learning model, to capture global node information while incorporating temporal local path counts as a key feature. Unlike other global feature-capturing methods,$PathSAGE$optimises computational complexity. Experimental results on thirteen real-world temporal networks demonstrate that$PathSAGE$outperforms the state-of-the-art methods in accurately identifying influential spreaders.$PathSAGE$exhibits a strong correlation with the Temporal Susceptible–Infected–Recovered ($TSIR$) model and achieves a relative improvement percentage ($\eta \%$) ranging from 0.12% to 70.70%. Additionally,$PathSAGE$attains the lowest average robustness value of 0.17, highlighting its effectiveness in identifying influential spreaders within temporal networks.