While widely used for information exchange, social media networks are increasingly exploited to spread rumors and malicious content. It is essential to reconstruct propagation graphs to hold nodes accountable for such propagation. However, the dynamic and vast nature of social networks makes this task challenging. This study introduces Structural Hidden Node Identification (SHNI), a heuristic method for reconstructing partially observed propagation snapshots by identifying likely hidden participants. SHNI relies on the observed active nodes and structural neighborhood information, without requiring temporal traces, message content, user attributes, or network-specific supervised retraining; its structural parameters may be selected by expert knowledge or calibrated once and then reused across networks. Instead of materializing the entire graph in advance, the method supports on-demand neighborhood expansion from observed active nodes toward structurally relevant candidate nodes. It combines local activation pressure with a structural bridging component inspired by sociological principles of opinion formation, including local social influence, information bubbles, and the role of bridging nodes between groups. SHNI explores candidate hidden nodes through a priority-driven neighborhood expansion strategy, in which the most promising candidates are evaluated first, and further exploration stops when the heuristic evidence becomes insufficient. Experiments across different network structures show that SHNI achieves competitive reconstruction quality while maintaining low data requirements and transparent decision logic. These results indicate that SHNI is especially useful as an interpretable structural baseline when temporal traces, message content, or network-specific supervised training data are unavailable. The method may also be adapted to other propagation phenomena, such as epidemic spread or malware diffusion in computer networks.