Abstract
We consider new models of activation/influence propagation in networks based on the concept of double thresholds: a node will “activate” if at least a certain minimum fraction of its neighbors are active and no more than a certain maximum fraction of neighbors are active. These models are more flexible than standard threshold models as they allow to incorporate more complex dynamics of diffusion processes when nodes can activate and deactivate, which have possible interpretations, for instance, in the context of communication and social networks. In a social network, consistently with the hypothesis originally mentioned by Granovetter (1978), a person may “activate” (e.g., adopt and/or repost an opinion) if sufficiently many but not too many of their friends (i.e., neighbors in a network) have adopted this opinion. We study several versions of this problem setup under different assumptions on activation/deactivation mechanisms and initial choices of seed nodes, and compare the results to the well-known “single threshold” (e.g., linear threshold) models.
This material is based on work supported by the AFRL Mathematical Modeling and Optimization Institute.
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Semenov, A., Veremyev, A., Pasiliao, E.L., Boginski, V. (2020). Double-Threshold Models for Network Influence Propagation. In: Chellappan, S., Choo, KK.R., Phan, N. (eds) Computational Data and Social Networks. CSoNet 2020. Lecture Notes in Computer Science(), vol 12575. Springer, Cham. https://doi.org/10.1007/978-3-030-66046-8_42
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