  Social distance games have been extensively studied as a coalition formation
model where the utilities of agents in each coalition were captured using a
utility function u that took into account distances in a given social network.
In this paper, we consider a non-normalized score-based definition of social
distance games where the utility function u_v depends on a generic scoring
vector v, which may be customized to match the specifics of each individual
application scenario.
  As our main technical contribution, we establish the tractability of
computing a welfare-maximizing partitioning of the agents into coalitions on
tree-like networks, for every score-based function u_v. We provide more
efficient algorithms when dealing with specific choices of u_v or simpler
networks, and also extend all of these results to computing coalitions that are
Nash stable or individually rational. We view these results as a further strong
indication of the usefulness of the proposed score-based utility function: even
on very simple networks, the problem of computing a welfare-maximizing
partitioning into coalitions remains open for the originally considered
canonical function u.
