  Exploring the complex structure of the human brain is crucial for
understanding its functionality and diagnosing brain disorders. Thanks to
advancements in neuroimaging technology, a novel approach has emerged that
involves modeling the human brain as a graph-structured pattern, with different
brain regions represented as nodes and the functional relationships among these
regions as edges. Moreover, graph neural networks (GNNs) have demonstrated a
significant advantage in mining graph-structured data. Developing GNNs to learn
brain graph representations for brain disorder analysis has recently gained
increasing attention. However, there is a lack of systematic survey work
summarizing current research methods in this domain. In this paper, we aim to
bridge this gap by reviewing brain graph learning works that utilize GNNs. We
first introduce the process of brain graph modeling based on common
neuroimaging data. Subsequently, we systematically categorize current works
based on the type of brain graph generated and the targeted research problems.
To make this research accessible to a broader range of interested researchers,
we provide an overview of representative methods and commonly used datasets,
along with their implementation sources. Finally, we present our insights on
future research directions. The repository of this survey is available at
\url{https://github.com/XuexiongLuoMQ/Awesome-Brain-Graph-Learning-with-GNNs}.
