  There is a fast-growing body of research on predicting future links in
dynamic networks, with many new algorithms. Some benchmark data exists, and
performance evaluations commonly rely on comparing the scores of observed
network events (positives) with those of randomly generated ones (negatives).
These evaluation measures depend on both the predictive ability of the model
and, crucially, the type of negative samples used. Besides, as generally the
case with temporal data, prediction quality may vary over time. This creates a
complex evaluation space. In this work, we catalog the possibilities for
negative sampling and introduce novel visualization methods that can yield
insight into prediction performance and the dynamics of temporal networks. We
leverage these visualization tools to investigate the effect of negative
sampling on the predictive performance, at the node and edge level. We validate
empirically, on datasets extracted from recent benchmarks that the error is
typically not evenly distributed across different data segments. Finally, we
argue that such visualization tools can serve as powerful guides to evaluate
dynamic link prediction methods at different levels.
