Influence maximization in social networks under Deterministic Linear Threshold Model

Abstract

We define the new Targeted and Budgeted Influence Maximization under Deterministic Linear Threshold Model problem and develop the novel and scalable TArgeted and BUdgeted Potential Greedy (TABU-PG) algorithm which allows for optional methods to solve this problem. It is an iterative and greedy algorithm that relies on investing in potential future gains when choosing seed nodes. We suggest new real-world mimicking techniques for generating influence weights, thresholds, profits, and costs. Extensive computational experiments on four real network (Epinions, Academia, Pokec and Inploid) show that our proposed heuristics perform significantly better than benchmarks. We equip TABU-PG with novel scalability methods which reduce runtime by limiting the seed node candidate pool, or by selecting more nodes at once, trading-off with spread performance.