  Temporal networks model a variety of important phenomena involving timed
interactions between entities. Existing methods for machine learning on
temporal networks generally exhibit at least one of two limitations. First,
time is assumed to be discretized, so if the time data is continuous, the user
must determine the discretization and discard precise time information. Second,
edge representations can only be calculated indirectly from the nodes, which
may be suboptimal for tasks like edge classification. We present a simple
method that avoids both shortcomings: construct the line graph of the network,
which includes a node for each interaction, and weigh the edges of this graph
based on the difference in time between interactions. From this derived graph,
edge representations for the original network can be computed with efficient
classical methods. The simplicity of this approach facilitates explicit
theoretical analysis: we can constructively show the effectiveness of our
method's representations for a natural synthetic model of temporal networks.
Empirical results on real-world networks demonstrate our method's efficacy and
efficiency on both edge classification and temporal link prediction.
