  Recently, Network Embedding (NE) has become one of the most attractive
research topics in machine learning and data mining. NE approaches have
achieved promising performance in various of graph mining tasks including link
prediction and node clustering and classification. A wide variety of NE methods
focus on the proximity of networks. They learn community-oriented embedding for
each node, where the corresponding representations are similar if two nodes are
closer to each other in the network. Meanwhile, there is another type of
structural similarity, i.e., role-based similarity, which is usually
complementary and completely different from the proximity. In order to preserve
the role-based structural similarity, the problem of role-oriented NE is
raised. However, compared to community-oriented NE problem, there are only a
few role-oriented embedding approaches proposed recently. Although less
explored, considering the importance of roles in analyzing networks and many
applications that role-oriented NE can shed light on, it is necessary and
timely to provide a comprehensive overview of existing role-oriented NE
methods. In this review, we first clarify the differences between
community-oriented and role-oriented network embedding. Afterwards, we propose
a general framework for understanding role-oriented NE and a two-level
categorization to better classify existing methods. Then, we select some
representative methods according to the proposed categorization and briefly
introduce them by discussing their motivation, development and differences.
Moreover, we conduct comprehensive experiments to empirically evaluate these
methods on a variety of role-related tasks including node classification and
clustering (role discovery), top-k similarity search and visualization using
some widely used synthetic and real-world datasets...
