Influence maximization under limited network information: Seeding high-degree neighbors

Autor:	Ou, Jiamin, Buskens, Vincent, Van De Rijt, Arnout, Panja, Debabrata
Rok vydání:	2022
Předmět:	Physics - Physics and Society Computer Science - Social and Information Networks Statistics - Methodology
Druh dokumentu:	Working Paper
Popis:	The diffusion of information, norms, and practices across a social network can be initiated by compelling a small number of seed individuals to adopt first. Strategies proposed in previous work either assume full network information or large degree of control over what information is collected. However, privacy settings on the Internet and high non-response in surveys often severely limit available connectivity information. Here we propose a seeding strategy for scenarios with limited network information: Only the degrees and connections of some random nodes are known. This new strategy is a modification of "random neighbor sampling" and seeds the highest-degree neighbors of randomly selected nodes. In simulations of a linear threshold model on a range of synthetic and real-world networks, we find that this new strategy outperforms other seeding strategies, including high-degree seeding and clustered seeding. Comment: 28 pages, 9 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2202.03893 Zobrazit plný text záznamu View this record from Arxiv