Multi-objective Biogeography-Based Optimization for Influence Maximization-Cost Minimization in Social Networks

The influence maximization, which selects a set of k users (called seed set) from a social network to maximize the expected number of influenced users (called influence spread), is a key algorithmic problem in social influence analysis. In the past, a lot of studies were carried out to identify influential seeds from a given social graph and propagation model. Many propagation models, greedy algorithms, approximation algorithms came. However, a very less effort was made towards influence maximization-cost minimization problem. Therefore in this work, we have suggested a multi-objective biography-based optimization strategy to maximize influence while minimizing the cost. The strategy combines the best attributes of biogeography-based optimization and non-dominated sorting genetic algorithm II. A multi-objective ranking and selection strategy improve the convergence rate. Our empirical analysis on many real-life networks confers the effectiveness of the algorithm in terms of both influence spread and time efficiency.