  Learning low-dimensional topological representation of a network in dynamic
environments is attracting much attention due to the time-evolving nature of
many real-world networks. The main and common objective of Dynamic Network
Embedding (DNE) is to efficiently update node embeddings while preserving
network topology at each time step. The idea of most existing DNE methods is to
capture the topological changes at or around the most affected nodes (instead
of all nodes) and accordingly update node embeddings. Unfortunately, this kind
of approximation, although can improve efficiency, cannot effectively preserve
the global topology of a dynamic network at each time step, due to not
considering the inactive sub-networks that receive accumulated topological
changes propagated via the high-order proximity. To tackle this challenge, we
propose a novel node selecting strategy to diversely select the representative
nodes over a network, which is coordinated with a new incremental learning
paradigm of Skip-Gram based embedding approach. The extensive experiments show
GloDyNE, with a small fraction of nodes being selected, can already achieve the
superior or comparable performance w.r.t. the state-of-the-art DNE methods in
three typical downstream tasks. Particularly, GloDyNE significantly outperforms
other methods in the graph reconstruction task, which demonstrates its ability
of global topology preservation. The source code is available at
https://github.com/houchengbin/GloDyNE
