  Learning topological representation of a network in dynamic environments has
recently attracted considerable attention due to the time-evolving nature of
many real-world networks i.e. nodes/links might be added/removed as time goes
on. Dynamic network embedding aims to learn low dimensional embeddings for
unseen and seen nodes by using any currently available snapshots of a dynamic
network. For seen nodes, the existing methods either treat them equally
important or focus on the $k$ most affected nodes at each time step. However,
the former solution is time-consuming, and the later solution that relies on
incoming changes may lose the global topology---an important feature for
downstream tasks. To address these challenges, we propose a dynamic network
embedding method called DynWalks, which includes two key components: 1) An
online network embedding framework that can dynamically and efficiently learn
embeddings based on the selected nodes; 2) A novel online node selecting scheme
that offers the flexible choices to balance global topology and recent changes,
as well as to fulfill the real-time constraint if needed. The empirical studies
on six real-world dynamic networks under three different slicing ways show that
DynWalks significantly outperforms the state-of-the-art methods in graph
reconstruction tasks, and obtains comparable results in link prediction tasks.
Furthermore, the wall-clock time and complexity analysis demonstrate its
excellent time and space efficiency. The source code of DynWalks is available
at https://github.com/houchengbin/DynWalks
