A multi-agent solution to maximizing product adoption in dynamic social networks

It is an interesting problem in a social system investigating how to affect a large number of people by just investing on a minority of them. This problem, i.e., influence maximization, is called “maximizing product adoption” in marketing applications. In this paper, we first propose a multi-agent framework called MAFIM to be used for maximizing product adoption in dynamic social networks. MAFIM consists of two types of agents: modeling agents and solution provider agents. These agents view a dynamic social network as consecutive static network snapshots and regarding that, choose a budget assignment policy so that each snapshot obtains its share from the budget defined by the sales manager. Based on MAFIM, we present MASPEL, a single product model which takes network communities, their judgments on each other and their profitabilities into account. MASPEL makes use of a specific budget assignment policy in which budgets are assigned to advertisement campaigns in a progressively decreasing manner. We applied our model on several real and synthetic dynamic social networks then evaluated the effectiveness of different campaign lengths. Our results show that it is more effective to launch many short-lived campaigns instead of few long-lived ones. It was also observed that betweenness has the best performance among centrality-based heuristics in leading the majority towards liking the advertised product.