Dynamic Hypergraph Source Identification via Topology-aware Mamba and Diffusion Supervision

Source identification in dynamic hypergraphs is challenging because evolving hyperedges continuously alter the available propagation paths, making diffusion dynamics strongly dependent on structural evolution. Existing methods mainly learn diffusion patterns from snapshot sequences, but often overlook dynamic higher-order interactions. Therefore, we propose a framework for dynamic hypergraph source identification via Topology-guided Mamba and Diffusion Supervision (TMDS). To capture the hyperedge evolution, the hyperedge evolution modeling module first characterizes node-level structural evolution from participation scale, neighbor composition, and group organization. These transition signals are then incorporated into a dynamic topology-aware Mamba, where retention and propagation gates adapt temporal inference to evolving higher-order structures. Furthermore, a forward diffusion supervision mechanism reconstructs the diffusion trajectory from the predicted source distribution and feeds the reconstruction discrepancy back to the inference model. Experiments on four diverse datasets demonstrate consistent improvements over representative baselines and verify the effectiveness of the proposed approach.