A Positive Influence Maximization Algorithm in Signed Social Networks

The influence maximization (IM) problem aims to find a set of seed nodes that maximizes the spread their in social network. positive (PIM) is an extension IM problem, which consider polar relation signed networks so seeds can be most widely spread. To solve PIM this paper proposes and decay related independent cascade (IC-PD) model simulate propagation information during networks. overcome low efficiency greedy based algorithm, defines reverse reachable (PRR) devises sampling (SRIS) algorithm. algorithm utilizes IC-PD as well PRR select seeds. There are two phases SRIS. One phase, generate binary search calculate number needed sets. other node selection uses coverage optimal Finally, Experiments on three real-world network datasets demonstrate SRIS outperforms baseline algorithms effectiveness. Especially Slashdot dataset, achieves 24.7% higher performance than best-performing compared under weighted when size 25.