# Influential node identification

Influential-node identification in complex networks now has a cross-scale approach. Centrality-based and learning-based methods often integrate structural information across network scales only to a limited degree, so local, community-level and global patterns are not used together. CSBA-Net extracts structural representations at the three scales, models dependencies between them with a BiGRU and multi-head self-attention, and learns node influence scores by regression against SIR diffusion simulations; on 24 real and 12 synthetic networks its ranking metrics exceed the compared centrality, CNN and GNN baselines.

Influential-node identification in complex networks now has a cross-scale approach. Centrality-based and learning-based methods often integrate structural information across network scales only to a limited degree, so local, community-level and global patterns are not used together. CSBA-Net extracts structural representations at the three scales, models dependencies between them with a BiGRU and multi-head self-attention, and learns node influence scores by regression against SIR diffusion simulations; on 24 real and 12 synthetic networks its ranking metrics exceed the compared centrality, CNN and GNN baselines.