Improving Information Spread Through A Scheduled Seeding Approach

One highly studied aspect of social networks is the identification of influential nodes that can spread ideas in a highly efficient way. The vast majority of works in this field have investigated the problem of identifying a set of nodes, that if "seeded" simultaneously, would maximize the information spread in the network. Yet, the timing aspect, namely, finding not only which nodes should be seeded but also when to seed them, has not been sufficiently addressed. In this work, we revisit the problem of network seeding and demonstrate by simulations how an approach takes takes into account the timing aspect, can improve the rates of spread by over 23% compared to existing seeding methods. Such an approach has a wide range of applications, especially in cases where the network topology is easily accessible.