  Dynamic Network Embedding (DNE) has recently attracted considerable attention
due to the advantage of network embedding in various fields and the dynamic
nature of many real-world networks. An input dynamic network to DNE is often
assumed to have smooth changes over snapshots, which however would not hold for
all real-world scenarios. It is natural to ask if existing DNE methods can
perform well for an input dynamic network without smooth changes. To quantify
it, an index called Degree of Changes (DoCs) is suggested so that the smaller
DoCs indicates the smoother changes. Our comparative study shows several DNE
methods are not robust enough to different DoCs even if the corresponding input
dynamic networks come from the same dataset, which would make these methods
unreliable and hard to use for unknown real-world applications. To propose an
effective and more robust DNE method, we follow the notion of ensembles where
each base learner adopts an incremental Skip-Gram embedding model. To further
boost the performance, a simple yet effective strategy is designed to enhance
the diversity among base learners at each timestep by capturing different
levels of local-global topology. Extensive experiments demonstrate the superior
effectiveness and robustness of the proposed method compared to
state-of-the-art DNE methods, as well as the benefits of special designs in the
proposed method and its scalability.
