  With the prevalence of social media, the connectedness between people has
been greatly enhanced. Real-world relations between users on social media are
often not limited to expressing positive ties such as friendship, trust, and
agreement, but they also reflect negative ties such as enmity, mistrust, and
disagreement, which can be well modelled by signed graphs. Signed Graph
Representation Learning (SGRL) is an effective approach to analyze the complex
patterns in real-world signed graphs with the co-existence of positive and
negative links. In recent years, SGRL has witnesses fruitful results. SGRL
tries to allocate low-dimensional representations to nodes and edges which
could preserve the graph structure, attribute and some collective properties,
e.g., balance theory and status theory. To the best of knowledge, there is no
survey paper about SGRL up to now. In this paper, we present a broad review of
SGRL methods and discuss some future research directions.
