  Increased attention has been paid over the last four years to dynamic network
embedding. Existing dynamic embedding methods, however, consider the problem as
limited to the evolution of a topology over a sequence of global, discrete
states. We propose a novel embedding algorithm, WalkingTime, based on a
fundamentally different handling of time, allowing for the local consideration
of continuously occurring phenomena; while others consider global time-steps to
be first-order citizens of the dynamic environment, we hold flows comprised of
temporally and topologically local interactions as our primitives, without any
discretization or alignment of time-related attributes being necessary.
  Keywords: dynamic networks , representation learning , dynamic graph
embedding , time-respecting paths , temporal-topological flows , temporal
random walks , temporal networks , real-attributed knowledge graphs , streaming
graphs , online networks , asynchronous graphs , asynchronous networks , graph
algorithms , deep learning , network analysis , datamining , network science
