@inproceedings{4293e0a0803746498a5f902f5a2f7b3d,
title = "Deterministic influence maximization approach for sequential active marketing",
abstract = "The influence maximization problem aims to find the best seeding set of nodes in a network to increase the influence spread, under various information diffusion models. Recent advances have shown the importance of the timing of the seeding and introduced the sequential seeding approach, determining a step-by-step cascade of activations. Our study explores a novel Deterministic Influence Maximization Approach (DIMA) for time-based sequential seeding dynamics in a threshold-based model. We examine the problem characteristics and formulate solutions optimizing a scheduled sequential seeding strategy. Based on a set of empirical simulations we demonstrate the properties of the deterministic sequential problem, incorporate three different mathematical programming formulations and provide an initial benchmark for optimization techniques.",
keywords = "influence maximization, influence propagation, information diffusion, social networks",
author = "Dmitri Goldenberg and Tenzer, \{Eyal Tzvi\}",
note = "Publisher Copyright: {\textcopyright} 2021 ACM.; 13th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2021 ; Conference date: 08-11-2021",
year = "2021",
month = nov,
day = "8",
doi = "10.1145/3487351.3489474",
language = "English",
series = "Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2021",
publisher = "Association for Computing Machinery, Inc",
pages = "585--590",
editor = "Michele Coscia and Alfredo Cuzzocrea and Kai Shu",
booktitle = "Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2021",
}