# Multi-scale structural representation learning

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.

Remote sensing super-resolution previously injected low-resolution observations as dense spatial conditions into a diffusion Transformer, so small foreground objects were easily diluted by dominant backgrounds. MOSAIC organizes degraded observations into compact object-level and layout-level structural slots, fuses them across resolutions, and retrieves recurring structural priors from a shared prototype memory, then injects them into a frozen DiT4SR backbone through lightweight residual modulation. On UCMerced, AID and RSSCN7, PSNR is best at both scales, exceeding DiT4SR by 2.03 dB on UCMerced, 4.54 dB on AID and 1.49 dB on RSSCN7, with the lowest LPIPS on RSSCN7 and AID.

Remote sensing super-resolution previously injected low-resolution observations as dense spatial conditions into a diffusion Transformer, so small foreground objects were easily diluted by dominant backgrounds. MOSAIC organizes degraded observations into compact object-level and layout-level structural slots, fuses them across resolutions, and retrieves recurring structural priors from a shared prototype memory, then injects them into a frozen DiT4SR backbone through lightweight residual modulation. On UCMerced, AID and RSSCN7, PSNR is best at both scales, exceeding DiT4SR by 2.03 dB on UCMerced, 4.54 dB on AID and 1.49 dB on RSSCN7, with the lowest LPIPS on RSSCN7 and AID.