  Graphs are a powerful representation tool in machine learning applications,
with link prediction being a key task in graph learning. Temporal link
prediction in dynamic networks is of particular interest due to its potential
for solving complex scientific and real-world problems. Traditional approaches
to temporal link prediction have focused on finding the aggregation of dynamics
of the network as a unified output. In this study, we propose a novel
perspective on temporal link prediction by defining nodes as Newtonian objects
and incorporating the concept of velocity to predict network dynamics. By
computing more specific dynamics of each node, rather than overall dynamics, we
improve both accuracy and explainability in predicting future connections. We
demonstrate the effectiveness of our approach using two datasets, including 17
years of co-authorship data from PubMed. Experimental results show that our
temporal graph embedding dynamics approach improves downstream classification
models' ability to predict future collaboration efficacy in co-authorship
networks by 17.34% (AUROC improvement relative to the baseline model).
Furthermore, our approach offers an interpretable layer over traditional
approaches to address the temporal link prediction problem.
