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• W2949745531 abstract "The threshold model is widely used to study the propagation of opinions and technologies in social networks. In this model, individuals adopt the new behavior based on how many neighbors have already chosen it. Specifically, we consider the permanent adoption model where individuals that have adopted the new behavior cannot change their state. We study cascades under the threshold model on sparse random graphs with community structure to see whether the existence of communities affects the number of individuals who finally adopt the new behavior. When seeding a small number of agents with the new behavior, the community structure has little effect on the final proportion of people that adopt it, i.e., the contagion threshold is the same as if there were just one community. On the other hand, seeding a fraction of the population with the new behavior has a significant impact on the cascade with the optimal seeding strategy depending on how strongly the communities are connected. In particular, when the communities are strongly connected, seeding in one community outperforms the symmetric seeding strategy that seeds equally in all communities. We also investigate the problem of optimum seeding given a budget constraint, and propose a gradient-based heuristic seeding strategy. Our algorithm, numerically, dispels commonly held beliefs in the literature that suggest the best seeding strategy is to seed over the nodes with the highest number of neighbors." @default.
• W2949745531 created "2019-06-27" @default.
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• W2949745531 date "2016-06-02" @default.
• W2949745531 modified "2023-09-27" @default.
• W2949745531 title "Impact of Community Structure on Cascades" @default.
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• W2949745531 doi "https://doi.org/10.1145/2940716.2940741" @default.
• W2949745531 hasPublicationYear "2016" @default.
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