  Predicting edges in networks is a key problem in social network analysis and
involves reasoning about the relationships between nodes based on the
structural properties of a network. In particular, link prediction can be used
to analyse how a network will develop or - given incomplete information about
relationships - to discover "missing" links. Our approach to this problem is
rooted in cooperative game theory, where we propose a new, quasi-local approach
(i.e., one which considers nodes within some radius k) that combines
generalised group closeness centrality and semivalue interaction indices. We
develop fast algorithms for computing our measure and evaluate it on a number
of real-world networks, where it outperforms a selection of other
state-of-the-art methods from the literature. Importantly, choosing the optimal
radius k for quasi-local methods is difficult, and there is no assurance that
the choice is optimal. Additionally, when compared to other quasi-local
methods, ours achieves very good results even when given a suboptimal radius k
as a parameter.
