  Many real-world relations can be represented by signed networks with positive
links (e.g., friendships and trust) and negative links (e.g., foes and
distrust). Link prediction helps advance tasks in social network analysis such
as recommendation systems. Most existing work on link analysis focuses on
unsigned social networks. The existence of negative links piques research
interests in investigating whether properties and principles of signed networks
differ from those of unsigned networks, and mandates dedicated efforts on link
analysis for signed social networks. Recent findings suggest that properties of
signed networks substantially differ from those of unsigned networks and
negative links can be of significant help in signed link analysis in
complementary ways. In this article, we center our discussion on a challenging
problem of signed link analysis. Signed link analysis faces the problem of data
sparsity, i.e. only a small percentage of signed links are given. This problem
can even get worse when negative links are much sparser than positive ones as
users are inclined more towards positive disposition rather than negative. We
investigate how we can take advantage of other sources of information for
signed link analysis. This research is mainly guided by three social science
theories, Emotional Information, Diffusion of Innovations, and Individual
Personality. Guided by these, we extract three categories of related features
and leverage them for signed link analysis. Experiments show the significance
of the features gleaned from social theories for signed link prediction and
addressing the data sparsity challenge.
