  Link prediction in collaboration networks is often solved by identifying
structural properties of existing nodes that are disconnected at one point in
time, and that share a link later on. The maximally possible recall rate or
upper bound of this approach's success is capped by the proportion of links
that are formed among existing nodes embedded in these properties.
Consequentially, sustained ties as well as links that involve one or two new
network participants are typically not predicted. The purpose of this study is
to highlight formational constraints that need to be considered to increase the
practical value of link prediction methods for collaboration networks. In this
study, we identify the distribution of basic link formation types based on four
large-scale, over-time collaboration networks, showing that current link
predictors can maximally anticipate around 25% of links that involve at least
one prior network member. This implies that for collaboration networks,
increasing the accuracy of computational link prediction solutions may not be a
reasonable goal when the ratio of collaboration ties that are eligible to the
classic link prediction process is low.
