Time-Constrained Adaptive Influence Maximization

The well-known influence maximization problem (IM) aims at maximizing the of one information cascade in a social network by selecting appropriate seed users prior to diffusion process. In its adaptive version, additional can be selected after observing certain results. On other hand, computing tasks are often time-critical, and therefore, only resulted early period is worthwhile, which naturally modeled enforcing time constraint. this article, we present an analysis time-constrained IM problem. theory side, provide hardness results optimal policy lower bound on gap, measures superiority policies over nonadaptive policies. For practical solutions, from basic advanced, design series seeding for achieving high efficacy scalability. Finally, investigate proposed solutions through extensive simulations based real-world data sets.