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

Author:

Ou JiaminORCID,Buskens Vincent,van de Rijt Arnout,Panja DebabrataORCID

Abstract

Abstract 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 a 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’ (or ‘one-hop’) and seeds the highest-degree neighbors of randomly selected nodes. Simulating a fractional threshold model, we find that this new strategy excels in networks with heavy tailed degree distributions such as scale-free networks and large online social networks. It outperforms the conventional one-hop strategy even though the latter can seed 50% more nodes, and other seeding possibilities including pure high-degree seeding and clustered seeding.

Funder

Netherlands Organization for Scientific Research

Publisher

IOP Publishing

Subject

Artificial Intelligence,Computer Networks and Communications,Computer Science Applications,Information Systems

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