Modeling information propagation for target user groups in online social networks based on guidance and incentive strategies

The rapid development of online social networks has greatly facilitated the dissemination and sharing of information. Effectively guiding the propagation of information to specific target groups is a significant and challenging research issue, which can be formulated as the target propagation problem. Most existing studies, however, focus on traditional information propagation methods, treating all users in the network as target audiences, which results in low efficiency and high costs. To address this issue, we propose a novel information propagation model that incorporates adaptive guidance and incentive strategies, called the $SI{I}_{in}{R}_{gu}$ model, to simulate the target spreading process in online social networks. Our model is designed to enhance both global communication capabilities and information transmission efficiency by introducing a mutual influence score that quantifies the interaction between target and non-target users. Based on this, the $SI{I}_{in}{R}_{gu}$ model adaptively guides and incentivizes non-target users to disseminate information specifically to target user groups. We conducted several groups of experiments on nine real-world social networks, assessing scenarios with both single and multiple target groups. Experimental results demonstrate that the $SI{I}_{in}{R}_{gu}$ model outperforms existing methods in terms of target influence range and the effectiveness of information spreading, thereby offering valuable insights for practical applications.