  We have witnessed the discovery of many techniques for network representation
learning in recent years, ranging from encoding the context in random walks to
embedding the lower order connections, to finding latent space representations
with auto-encoders. However, existing techniques are looking mostly into the
local structures in a network, while higher-level properties such as global
community structures are often neglected. We propose a novel network
representations learning model framework called RUM (network Representation
learning throUgh Multi-level structural information preservation). In RUM, we
incorporate three essential aspects of a node that capture a network's
characteristics in multiple levels: a node's affiliated local triads, its
neighborhood relationships, and its global community affiliations. Therefore
the framework explicitly and comprehensively preserves the structural
information of a network, extending the encoding process both to the local end
of the structural information spectrum and to the global end. The framework is
also flexible enough to take various community discovery algorithms as its
preprocessor. Empirical results show that the representations learned by RUM
have demonstrated substantial performance advantages in real-life tasks.
