  Unsupervised graph representation learning(GRL) aims to distill diverse graph
information into task-agnostic embeddings without label supervision. Due to a
lack of support from labels, recent representation learning methods usually
adopt self-supervised learning, and embeddings are learned by solving a
handcrafted auxiliary task(so-called pretext task). However, partially due to
the irregular non-Euclidean data in graphs, the pretext tasks are generally
designed under homophily assumptions and cornered in the low-frequency signals,
which results in significant loss of other signals, especially high-frequency
signals widespread in graphs with heterophily. Motivated by this limitation, we
propose a multi-view perspective and the usage of diverse pretext tasks to
capture different signals in graphs into embeddings. A novel framework, denoted
as Multi-view Graph Encoder(MVGE), is proposed, and a set of key designs are
identified. More specifically, a set of new pretext tasks are designed to
encode different types of signals, and a straightforward operation is
propxwosed to maintain both the commodity and personalization in both the
attribute and the structural levels. Extensive experiments on synthetic and
real-world network datasets show that the node representations learned with
MVGE achieve significant performance improvements in three different downstream
tasks, especially on graphs with heterophily. Source code is available at
\url{https://github.com/G-AILab/MVGE}.
