  So far, many network-structure-based link prediction methods have been
proposed. However, these methods only highlight one or two structural features
of networks, and then use the methods to predict missing links in different
networks. The performances of these existing methods are not always satisfied
in all cases since each network has its unique underlying structural features.
In this paper, by analyzing different real networks, we find that the
structural features of different networks are remarkably different. In
particular, even in the same network, their inner structural features are
utterly different. Therefore, more structural features should be considered.
However, owing to the remarkably different structural features, the
contributions of different features are hard to be given in advance. Inspired
by these facts, an \emph{adaptive} fusion model regarding link prediction is
proposed to incorporate multiple structural features. In the model, a logistic
function combing multiple structural features is defined, then the weight of
each feature in the logistic function is \emph{adaptively} determined by
exploiting the known structure information. Last, we use the "learnt" logistic
function to predict the connection probabilities of missing links. According to
our experimental results, we find that the performance of our adaptive fusion
model is better than many similarity indices.
