AAAI Publications, Workshops at the Thirtieth AAAI Conference on Artificial Intelligence

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Simultaneous Influencing and Mapping for Health Interventions
Leandro Soriano Marcolino, Aravind Lakshminarayanan, Amulya Yadav, Milind Tambe

Last modified: 2016-03-29

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.

Keywords


Social networks; Influence maximization

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