A Data-Based Approach to Social Influence Maximization
Influence maximization is the problem of finding a set of users in a social network, such that by targeting this set, one maximizes the expected spread of influence in the network. Most of the literature on this topic has focused exclusively on the social graph, overlooking historical data, i.e., traces of past action propagations. In this paper, the authors study influence maximization from a novel data-based perspective. In particular, they introduce a new model, which they call credit distribution that directly leverages available propagation traces to learn how influence flows in the network and uses this to estimate expected influence spread.