Social influence modelling and maximization appear significant in various domains, such as e-business, marketing, and social computing. Most existing studies focus on how to maximize positive social impact to promote product adoptions based on static network snapshots. Such approaches can only increase influence in a social network in short-term, but cannot generate sustainable or long-term effects. In this research work, we study how to maintain long-term influence in a social network and propose an agent-based influence maintenance model, which can select influential nodes based on the current status in dynamic social networks in multiple times. Within the context of our investigation, the experimental results indicate that multiple-time seed selection is capable of achieving more constant impact than that of one-shot selection. We claim that influence maintenance is crucial for supporting, enhancing, and assisting long-term goals in business development. The proposed approach can automatically maintain long-lasting impact and achieve influence maintenance.

Service-oriented computing has been developing rapidly with various architectures and applications for improving consumer satisfaction. However, the increasing complexity of service models has brought many challenges to the trust evaluation accuracy, which is a decisive factor in making interactions between consumers and providers in dynamic environments. In composite services, one of the culprits for inaccurate trust evaluations is the problem of missing evidence caused by the rating convention. To address this, this paper proposes a strong evidence-based approach to manage ratings for members of composite services under uncertainty. Moreover, the approach complements the existing one-to-one towards one-to-many trust evaluation. The experimental results show that our method can enhance the consistency and accuracy of reputation systems, robust against biased ratings and also suitable for distributed environments.