Abstract: Graphs, as an important non-Euclidean data structure, are capable of modeling various entities and their complex relationships in the real world through interactions between nodes and edges. They have been widely applied in numerous domains, including social network analysis, citation network mining, traffic flow modeling, and financial risk control. Graph representation learning, which is central to mining the value of graph data, aims to map nodes, edges, or subgraphs into low-dimensional, dense vectors while preserving the structural and attribute information within the graph, providing high-quality representations for downstream tasks such as node classification and link prediction. In recent years, although deep learning-based graph representation learning has achieved significant progress, their performance is highly dependent on the quality of the input data. However, complex graph data suffer from distribution imbalances and quality defects in real-world scenarios, such as class imbalance, topological imbalance, label sparsity, and noise interference. Currently, there is a lack of systematic reviews and summaries on robust graph representation learning methods. Therefore, this paper aims to organize recent robust graph representation learning methods that address the aforementioned challenges. We first introduce relevant background knowledge and provide formal definitions. Subsequently, we review robust graph representation learning methods targeting class imbalance, topological imbalance, label sparsity, and noise interference. Finally, we discuss the current challenges faced by robust graph representation methods and prospect future research directions.