Credit Distribution and influence maximization in online social networks using node features

Influence maximization is a problem of identifying a small set of highly influential individuals such that obtaining the maximized influence spread after propagation in social networks. How to evaluate the influence is essential to solve the influence maximization problem. Meanwhile, finding out influence propagation paths is one of key factors in the assessment of influence spread. However, since most of existent models and algorithms use degrees to simplify the activation probability on edges, node features are always ignored in the evaluation of influence ability for different users. In this paper, besides the node features, the Credit Distribution (CD) model is extended to incorporate the time-critical aspect of influence in social networks. After assigning credit along with the action propagation paths, we pick up the nodes which have maximal marginal gain in each iteration to form the seed set. The experiments we performed on real online social networks demonstrate that our approach is efficiency and reasonability for identifying seed sets, and the influence spread prediction by our approach is more accurately than that of original algorithm which disregards the node features in the influence evaluation and diffusion.