Why spending more might get you less, dynamic selection of influencers in social networks

Many studies in the field of information spread through social networks focus on the detection of influencers. The spread dynamics in most of these studies assumes these influencers are first selected and 'infected' with a message, and then this message spreads through the networks by a viral process. The following work presents some difficulties with this separation between the infection stage and the viral stage, and provides a case where an increased effort spent on the spread of an idea results in lower final rates of spread. Such results can be prevented by the Scheduling Seeding approach. This approach gradually plans the timing of infection for each particular node as the viral process progresses. It outperforms the initial seeding approach, and prevents the occurrence of the counter-intuitive (and unwanted) results where a greater effort results in a less successful spread. A simple but effective heuristics to detect what node to seed and where is provided.