Identifying hidden target nodes for spreading in complex networks

Using measurable data to realize targeted spreading of vital nodes in complex networks is an important issue connecting to various real applications such as commercial advertising, medication selection, and even military attack. However, a significant challenge is that the target nodes are not always known, which hinders the best allocation of initial spreaders to maximize the affected target nodes. To address this issue, this study develops a general framework to map the target node identification problem to the solution of underdetermined equations. Similar to the sparse signal reconstruction problem, it can be solved by the standard compressed sensing algorithm. Our research is completely driven by the limited data fed back after each spread realization. The experimental results show that this decoding method can efficiently achieve a high calculation accuracy both in the artificial networks and the actual networks. Finally, the effects of network structure, infection probability and initial spreader on the accuracy are discussed, aiming to provide theoretical guidance and new enlightenment for practical applications.