Adding links boosts social media influence rankings significantly
Influence Ranking Improvement via Link Addition in Social Networks
Social and Information Networks
Summary
Social media users want to be seen as influential for sharing information. This paper looks at whether adding a few new connections from a user can improve their influence rank. The authors test two ways to pick these new links: one that tries hard to improve rank, and another that guesses randomly but runs faster. They find that even a small number of added links can move a user's rank much higher, showing that influence rankings can be changed by adding just a few new connections. The faster random method works well if more new links can be added.
What this means in practice
- •For social media marketers: Identify key users whose influence rankings can be boosted efficiently by adding targeted social links to improve campaign reach.
- •For online community managers: Adjust connectivity of user profiles to strategically enhance rankings of influential members and foster information spread.
Authors
Taiki Ikemoto, Sho Tsugawa
Abstract
Social media platforms increasingly rely on influential users for information dissemination in domains such as marketing and political campaigns. As the value of being recognized as influential grows, users may have incentives to strategically enhance their influence. In this study, we investigate whether and to what extent the influence ranking of a target node can be improved by adding a limited number of outgoing links in the context of influence maximization (IM). We formulate the Link Selection Problem for Influence Ranking Improvement (LSP-IRI) as the problem of selecting a fixed-size set of additional outgoing links from a target node in order to maximize its rank improvement. To examine this problem, we consider two representative heuristic strategies: a greedy method that directly optimizes rank improvement and a computationally efficient random search method. We conduct experiments on four real-world networks ranging from thousands to hundreds of thousands of nodes. The results show that even a few added links can substantially improve IM-based influence rankings. In particular, the greedy method improves the ranks of nodes initially ranked around 50 by several tens of positions on average with only three added links, sometimes moving them into the top 10. The random search method achieves smaller gains under strict budgets but reduces computation time by up to approximately 98% compared with the greedy method and becomes effective when larger budgets are allowed. These findings show that IM-based influence rankings are sensitive to limited local structural modifications and highlight a trade-off between ranking improvement and computational efficiency.