Traditional centrality measures normally rely on a single topological scale, which limits their ability to comprehensively characterize the roles of nodes across local, semi-local, and global network structures. Moreover, their generalization capability across different network types is often weak, leading to low accur...
The existing key node identification methods in complex networks rely on local topological information or global structural features, which are difficult to fully integrate multi-order local structural information and global propagation characteristics, resulting in the limitation of the accuracy and discrimination abi...
A Multi-Granularity Implicit Temporal (MGIT) framework that enhances temporal representation and reasoning by modeling implicit temporal dependencies across different granularities is proposed, highlighting its effectiveness in capturing implicit temporal information and enhancing multi-granularity temporal reasoning.