  Representation learning models for graphs are a successful family of
techniques that project nodes into feature spaces that can be exploited by
other machine learning algorithms. Since many real-world networks are
inherently dynamic, with interactions among nodes changing over time, these
techniques can be defined both for static and for time-varying graphs. Here, we
build upon the fact that the skip-gram embedding approach implicitly performs a
matrix factorization, and we extend it to perform implicit tensor factorization
on different tensor representations of time-varying graphs. We show that
higher-order skip-gram with negative sampling (HOSGNS) is able to disentangle
the role of nodes and time, with a small fraction of the number of parameters
needed by other approaches. We empirically evaluate our approach using
time-resolved face-to-face proximity data, showing that the learned
time-varying graph representations outperform state-of-the-art methods when
used to solve downstream tasks such as network reconstruction, and to predict
the outcome of dynamical processes such as disease spreading. The source code
and data are publicly available at https://github.com/simonepiaggesi/hosgns.
