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

TL;DR

This paper introduces a seeding strategy for influence maximization in social networks with limited information, leveraging high-degree neighbors of random nodes, and demonstrates its superior performance through simulations.

Contribution

It proposes a novel seeding method based on limited network data, improving influence spread over existing strategies.

Findings

Outperforms high-degree and clustered seeding strategies in simulations.

Effective under scenarios with partial network information.

Shows robustness across synthetic and real-world networks.

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 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.