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


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.


Social networks; Influence maximization

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