Two-Phase Influence Maximization in Social Networks with Seed Nodes and Referral Incentives

The problem of maximizing the spread of influence with a limited budget is central to social networks research. Most problems addressed in existing literature spend the entire budget towards triggering diffusion at seed nodes. In this paper, we investigate the effect of splitting the budget across two sequential phases; each phase corresponds to a different way of spreading influence. In phase 1, we adopt the classical approach of triggering diffusion at a selected set of seed nodes to spread the influence. In phase 2, we use the remaining budget to offer referral incentives. Assuming the independent cascade model, we formulate an objective function for the above two-phase influence maximization problem, and investigate its properties. We determine an effective budget-split between the two phases with detailed experiments on synthetic and real-world datasets. The principal findings from our study are: (a) when the budget is low, it is prudent to use the entire budget for phase 1; (b) when the budget is moderate to high, it is preferable to use much of the budget for phase 1, while allocating the remaining budget to phase 2; (c) in the presence of moderate to strict temporal constraints, phase 2 is not warranted; (d) if the temporal constraints are low or absent, referral incentives (phase 2) yield a decisive improvement in influence spread.