Simultaneous influencing and mapping for health interventions

Soriano Marcolino, Leandro and Lakshminarayanan, Aravind and Yadav, Amulya and Tambe, Milind (2016) Simultaneous influencing and mapping for health interventions. In: 3rd Workshop on Expanding the Boundaries of Health Informatics Using AI (HIAI'16). AAAI.

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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.

Item Type:
Contribution in Book/Report/Proceedings
ID Code:
81478
Deposited By:
Deposited On:
19 Sep 2016 09:48
Refereed?:
Yes
Published?:
Published
Last Modified:
05 Apr 2020 00:46