Abstract
We consider models for inducing a maximum cascade of activating connections in social networks over a finite horizon subject to budget constraints. These models reflect problems of choosing an initial set of pairs of individuals to connect or engage in order to maximize the cascade of new connections or engagements over time. We assume connections activate as a result of past activation of neighboring connections. We show that the optimization problem is NP-hard, and we provide a method for improving computations.
Supported by NRC Research Associateship Programs and the US Air Force Research Laboratory (AFRL) Mathematical Modeling and Optimization Institute and sponsored by AFRL/RW under agreement number FA8651-14-2-0002.
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The US Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright notation thereon. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of AFRL/RW or the US Government.
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Zenarosa, G.L., Veremyev, A., Pasiliao, E.L. (2019). Cascade of Edge Activation in Networks. In: Tagarelli, A., Tong, H. (eds) Computational Data and Social Networks. CSoNet 2019. Lecture Notes in Computer Science(), vol 11917. Springer, Cham. https://doi.org/10.1007/978-3-030-34980-6_16
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DOI: https://doi.org/10.1007/978-3-030-34980-6_16
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