Influence maximization in the presence of vulnerable nodes: a ratio perspective (Q2220831)

This paper studies influence maximization problem in social networks in the presence of both vulnerable and non-vulnerable users. The main target is to sort out key individuals to limit the influence to vulnerable individuals but maximize the influence to non-vulnerable individuals. The additively smoothed ratio is proposed as a measure to quantify the quality of a set. A greedy heuristic for finding a seed set with maximized additively smooths ratio is proposed. The authors have also designed approximation algorithms for finding a seed set with maximized additively smooths ratio. The relevant complexity and properties are examined. The paper includes some experimental validation and comparison of different strategies on Wiki-vote, Twitter and PolBlogs datasets.