Credit distribution for influence maximization in online social networks with node features

Influence maximization is a problem of identifying a small set of highly influential individuals such that obtaining the maximum value of influence spread 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 nodes' degrees are utilized by most of existent models and algorithms to estimate the activation probabilities 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 online social networks. After assigning credit along with the action propagation paths, we pick up the node which has maximal marginal gain in each iteration to form the seed set. The experiments we performed on real datasets demonstrate that our approach is efficient and reasonable for identifying seed nodes, and the influence spread prediction by our approach is more accurate than that of original method which disregards node features in the influence evaluation and diffusion process.