  One of the major issues in signed networks is to use network structure to
predict the missing sign of an edge. In this paper, we introduce a novel
probabilistic approach for the sign prediction problem. The main characteristic
of the proposed models is their ability to adapt to the sparsity level of an
input network. The sparsity of networks is one of the major reasons for the
poor performance of many link prediction algorithms, in general, and sign
prediction algorithms, in particular. Building a model that has an ability to
adapt to the sparsity of the data has not yet been considered in the previous
related works. We suggest that there exists a dilemma between local and global
structures and attempt to build sparsity adaptive models by resolving this
dilemma. To this end, we propose probabilistic prediction models based on local
and global structures and integrate them based on the concept of smoothing. The
model relies more on the global structures when the sparsity increases, whereas
it gives more weights to the information obtained from local structures for low
levels of the sparsity. The proposed model is assessed on three real-world
signed networks, and the experiments reveal its consistent superiority over the
state of the art methods. As compared to the previous methods, the proposed
model not only better handles the sparsity problem, but also has lower
computational complexity and can be updated using real-time data streams.
