Network representation learning: A macro and micro view

Graph is a universe data structure that is widely used to organize data in
real-world. Various real-word networks like the transportation network, social
and academic network can be represented by graphs. Recent years have witnessed
the quick development on representing vertices in the network into a
low-dimensional vector space, referred to as network representation learning.
Representation learning can facilitate the design of new algorithms on the
graph data. In this survey, we conduct a comprehensive review of current
literature on network representation learning. Existing algorithms can be
categorized into three groups: shallow embedding models, heterogeneous network
embedding models, graph neural network based models. We review state-of-the-art
algorithms for each category and discuss the essential differences between
these algorithms. One advantage of the survey is that we systematically study
the underlying theoretical foundations underlying the different categories of
algorithms, which offers deep insights for better understanding the development
of the network representation learning field.