A Computational Framework for Influence Networks: Application to Clergy Influence in HIV/AIDS Outreach

Strong social networks can encourage healthy behaviors. In this paper, we introduce a sociology-based computational framework for influence networks. The model construct is generic and is applicable to diverse social network analysis. We demonstrate its usage in calibrating the positive influence of church clergy in spreading HIV/AIDs information in a large metropolitan city. Five experiments are designed to contrast influence with respect to the interaction style between clergy and churchgoers. Competitive and non-competitive knowledge dissemination are also analyzed. The results show that when only one set of information exists, the spreading scope is directly proportional to the product of population size and the disease infection rate. When competing information is present, the importance of clergy in spreading the information decreases when the original propagation sources are ample. However, if sufficient interaction and trust are present among the clergy and the participants, the clergy's positive influence remains significant despite pre-existing knowledge. The generalized framework requires minimal regional data to establish the influence network. It provides useful policy insights for decision makers to determine effective avenues for information dissemination through community influencers.