  We introduce a general framework for leveraging graph stream data for
temporal prediction-based applications. Our proposed framework includes novel
methods for learning an appropriate graph time-series representation, modeling
and weighting the temporal dependencies, and generalizing existing embedding
methods for such data. While previous work on dynamic modeling and embedding
has focused on representing a stream of timestamped edges using a time-series
of graphs based on a specific time-scale (e.g., 1 month), we propose the notion
of an $\epsilon$-graph time-series that uses a fixed number of edges for each
graph, and show its superiority over the time-scale representation used in
previous work. In addition, we propose a number of new temporal models based on
the notion of temporal reachability graphs and weighted temporal summary
graphs. These temporal models are then used to generalize existing base
(static) embedding methods by enabling them to incorporate and appropriately
model temporal dependencies in the data. From the 6 temporal network models
investigated (for each of the 7 base embedding methods), we find that the top-3
temporal models are always those that leverage the new $\epsilon$-graph
time-series representation. Furthermore, the dynamic embedding methods from the
framework almost always achieve better predictive performance than existing
state-of-the-art dynamic node embedding methods that are developed specifically
for such temporal prediction tasks. Finally, the findings of this work are
useful for designing better dynamic embedding methods.
