  Information networks are ubiquitous and are ideal for modeling relational
data. Networks being sparse and irregular, network embedding algorithms have
caught the attention of many researchers, who came up with numerous embeddings
algorithms in static networks. Yet in real life, networks constantly evolve
over time. Hence, evolutionary patterns, namely how nodes develop itself over
time, would serve as a powerful complement to static structures in embedding
networks, on which relatively few works focus. In this paper, we propose EPNE,
a temporal network embedding model preserving evolutionary patterns of the
local structure of nodes. In particular, we analyze evolutionary patterns with
and without periodicity and design strategies correspondingly to model such
patterns in time-frequency domains based on causal convolutions. In addition,
we propose a temporal objective function which is optimized simultaneously with
proximity ones such that both temporal and structural information are
preserved. With the adequate modeling of temporal information, our model is
able to outperform other competitive methods in various prediction tasks.
