This study aims to learn robust graph representations from unlabeled graph data. A novel framework, termed structural relation modeling (SRM), is proposed for self-supervised graph representation learning to alleviate inherent limitations caused by unlabeled data and graph topological imbalances. First, rather than focusing solely on local structures or node embeddings as in most existing methods, this study models complex structural relations, such as local-global relations and node correlations, among nodes, subgraphs, and entire graphs within a unified framework to better capture graph topology and utilize structural self-supervision. Second, a partition-based subgraph sampling mechanism is introduced to mitigate over-aggregation and topological decay induced by graph topological imbalance, enabling more uniform information propagation through mini-batch training. Third, a node regularization strategy is applied to improve training stability and efficiency, resulting in more accurate structural representations. Extensive experiments on node and graph classification across 12 public datasets demonstrate the effectiveness and generalizability of the proposed method.