  Graph Machine Learning (Graph ML) has witnessed substantial advancements in
recent years. With their remarkable ability to process graph-structured data,
Graph ML techniques have been extensively utilized across diverse applications,
including critical domains like finance, healthcare, and transportation.
Despite their societal benefits, recent research highlights significant safety
concerns associated with the widespread use of Graph ML models. Lacking
safety-focused designs, these models can produce unreliable predictions,
demonstrate poor generalizability, and compromise data confidentiality. In
high-stakes scenarios such as financial fraud detection, these vulnerabilities
could jeopardize both individuals and society at large. Therefore, it is
imperative to prioritize the development of safety-oriented Graph ML models to
mitigate these risks and enhance public confidence in their applications. In
this survey paper, we explore three critical aspects vital for enhancing safety
in Graph ML: reliability, generalizability, and confidentiality. We categorize
and analyze threats to each aspect under three headings: model threats, data
threats, and attack threats. This novel taxonomy guides our review of effective
strategies to protect against these threats. Our systematic review lays a
groundwork for future research aimed at developing practical, safety-centered
Graph ML models. Furthermore, we highlight the significance of safe Graph ML
practices and suggest promising avenues for further investigation in this
crucial area.
