The integration of physical and cyber networks can be a double-edged dynamic. While it accelerates the dissemination of positive information, it also intensifies the diffusion of negative information. A clear understanding of the stochastic dynamics of competitive information dissemination is essential. It enables effective control strategies to curb the spread of negative information and maintain social stability. However, designing and controlling the dissemination of competitive information in cyber–physical integrated networks (CPINs) is challenging due to competition and stochasticity. Moreover, the heterogeneity of CPINs further aggravates this problem. To address this, we propose a competitive information dissemination method to capture and control the stochastic dynamics in CPINs. Specifically, we build a CPIN model to characterize the heterogeneity between physical and cyber networks. Furthermore, we develop a temporal point process-based competitive information dissemination model (TPP-CIDM) that captures the stochastic evolution of both positive and negative information. This dissemination model quantifies the stochastic dynamics by computing the probability distribution of the sizes of competitive information, reducing biases inherent in deterministic solutions. Finally, we design an event-driven optimal control (EOC) strategy to dynamically modulate the intervention intensity. The intervention optimization problem is formulated to maximize utility under cost constraints, and a heuristic solution is provided. Numerical simulations on both synthetic and real-world networks demonstrate the effectiveness of the proposed method.

Collaborative crowdsensing (CCS) requires the recruited team to collaborate closely to complete sensing tasks with high quality of service (QoS). The team recruitment of CCS is mainly influenced by the subjective willingness of participants and the objective trust evaluation of the sensing platform; that is, the higher the subjective mutual willingness to work together and the objective mutual trust among participants, the more efficiency with which the CCS tasks will be achieved. However, the existing research lacks comprehensive consideration of mutual willingness and mutual trust among recruited participants. This results in poor QoS. To address this problem, we propose a novel team recruitment method for CCS that jointly considers the willingness and trust to recruit optimal teams. First, we build a graph convolutional network‐based willingness‐trust network (GCN‐WTN) model for CCS to obtain mutual willingness and trust among participants more accurately. Second, we propose a willingness and trust‐based team recruitment (WT‐TR) method to recruit the optimal teams for CCS. This method introduces the consensus and similarity constraints into the willingness and trust networks to better meet the collaboration needs of CCS. Finally, we implement a recruitment simulation platform for CCS to simulate the team recruitment process and validate the effectiveness of our proposed method. The experimental results show that the teams recruited by the proposed method can significantly improve QoS for CCS.