Influence maximization in the presence of vulnerable nodes: A ratio perspective

Influence maximization is a key problem seeking to identify users who will diffuse information influence the largest number of other in social network. A drawback that it could be socially irresponsible many whom would harmed, due their demographics, health conditions, or socioeconomic characteristics (e.g., predominantly overweight people influenced buy junk food). Motivated by this and fact some these vulnerable inadvertently, we introduce finding set (seeds) limits while maximizing non-vulnerable users. We define measure captures quality seeds as an additively smoothed ratio (ASR) between expected Then, develop methods which aim find maximizes measure: greedy heuristics, approximation algorithm, well several variations algorithm. evaluate our on synthetic real-world datasets demonstrate they substantially outperform state-of-the-art competitor terms both effectiveness efficiency. also algorithm offer different trade-offs