  Graph embedding has become an increasingly important technique for analyzing
graph-structured data. By representing nodes in a graph as vectors in a
low-dimensional space, graph embedding enables efficient graph processing and
analysis tasks like node classification, link prediction, and visualization. In
this paper, we propose a novel force-directed graph embedding method that
utilizes the steady acceleration kinetic formula to embed nodes in a way that
preserves graph topology and structural features. Our method simulates a set of
customized attractive and repulsive forces between all node pairs with respect
to their hop distance. These forces are then used in Newton's second law to
obtain the acceleration of each node. The method is intuitive, parallelizable,
and highly scalable. We evaluate our method on several graph analysis tasks and
show that it achieves competitive performance compared to state-of-the-art
unsupervised embedding techniques.
