  Real-world information networks are increasingly occurring across various
disciplines including online social networks and citation networks. These
network data are generally characterized by sparseness, nonlinearity and
heterogeneity bringing different challenges to the network analytics task to
capture inherent properties from network data. Artificial intelligence and
machine learning have been recently leveraged as powerful systems to learn
insights from network data and deal with presented challenges. As part of
machine learning techniques, graph embedding approaches are originally
conceived for graphs constructed from feature represented datasets, like image
dataset, in which links between nodes are explicitly defined. These traditional
approaches cannot cope with network data challenges. As a new learning
paradigm, network representation learning has been proposed to map a real-world
information network into a low-dimensional space while preserving inherent
properties of the network. In this paper, we present a systematic comprehensive
survey of network representation learning, known also as network embedding,
from birth to the current development state. Through the undertaken survey, we
provide a comprehensive view of reasons behind the emergence of network
embedding and, types of settings and models used in the network embedding
pipeline. Thus, we introduce a brief history of representation learning and
word representation learning ancestor of network embedding. We provide also
formal definitions of basic concepts required to understand network
representation learning followed by a description of network embedding
pipeline. Most commonly used downstream tasks to evaluate embeddings, their
evaluation metrics and popular datasets are highlighted. Finally, we present
the open-source libraries for network embedding.
