Roles within motifs improves key nodes identification in directed networks

Abstract Identifying key nodes is crucial in applications like epidemic control and information dissemination. Many conventional methods rely primarily on local or global topological properties, overlooking the role of intermediate higher-order structures, such as network motifs, particularly in directed networks where these patterns can reveal nuanced functional roles in propagation. To address this, we propose the Roles within Motifs (RM) method for identifying key nodes in directed networks. In this framework, roles within motifs are defined as r e c i p i e n t or d i s s e m i n a t o r , and a node’s propensity to act as a d i s s e m i n a t o r within motifs is quantified to capture its local higher-order structural information. Experiments on real-world networks show that RM significantly outperforms eight benchmark methods, with a 17.08% reduction in average ranking deviation in single-source propagation. Moreover, it attains near-optimal performance in multi-source propagation, demonstrating robust efficacy across tested conditions. Furthermore, the method has low computational complexity, making it suitable for large-scale network analysis.