Maximizing Influence on Social Networks with Conjugate Learning Automata

The problem of maximizing the spread of influence by selecting a subset of participants in a social network as sources, known as influence maximization, is a fruitful topic with straightforward application value. Greedy algorithms that select the optimal node one by one lay the foundation of follow-up research, and plentiful studies have been taken to improve the efficiency of greedy-based algorithms. However, the greedy methods can easily fall into adversary pitfalls, and corresponding improvements have been few. In this paper, a conjugate learning automata based method, utilizing the ability of cooperation in learning automata games, is proposed to obtain better-than-greedy propagation range. Comprehensive simulations in both synthetic and real-world datasets verify that the proposed method can attain better propagation range in some scenarios and is equally competitive respecting efficiency.