# Simultaneous Influencing and Mapping for Health Interventions

### Citation:

L. S. Marcolino, A. Lakshminarayanan, A. Yadav, and M. Tambe. 2016. “Simultaneous Influencing and Mapping for Health Interventions .” In 3rd Workshop on Expanding the Boundaries of Health Informatics Using AI (HIAI'16).
 2016_21_teamcore_aaai16.pdf 1.06 MB

### Abstract:

Influence Maximization is an active topic, but it was always assumed full knowledge of the social network graph. However, the graph may actually be unknown beforehand. For example, when selecting a subset of a homeless population to attend interventions concerning health, we deal with a network that is not fully known. Hence, we introduce the novel problem of simultaneously influencing and mapping (i.e., learning) the graph. We study a class of algorithms, where we show that: (i) traditional algorithms may have arbitrarily low performance; (ii) we can effectively influence and map when the independence of objectives hypothesis holds; (iii) when it does not hold, the upper bound for the influence loss converges to 0. We run extensive experiments over four real-life social networks, where we study two alternative models, and obtain significantly better results in both than traditional approaches.