<p>Community detection aims to uncover mesoscopic organization in graphs by grouping nodes into densely connected communities, and it plays an important role in analyzing citation networks, social systems, and other complex relational data. In practice, this task is challenging because community structures are determined not only by pairwise connectivity, but also by higher-order structural patterns such as motifs. Existing Graph Masked Autoencoders (GMAEs), although effective for self-supervised graph representation learning, usually rely on random node or edge masking and pairwise reconstruction objectives. As a result, they may fail to preserve the higher-order structural regularities that are crucial for identifying coherent communities. To address this issue, we propose a Motif-aware Graph Masked Autoencoder (MGMAE) for community detection. The proposed framework explicitly incorporates triangle motifs into both the masking and reconstruction processes. Specifically, MGMAE first enumerates motif structures as higher-order supervision targets, then constructs perturbed graphs through a joint motif–edge masking strategy, and finally learns node representations by jointly reconstructing masked edges and masked motifs. In this way, the learned embeddings are encouraged to preserve both local connectivity and higher-order cohesion patterns that are closely related to community organization. Extensive experiments on six real-world attributed networks and five synthetic LFR benchmarks demonstrate that MGMAE achieves highly competitive performance against representative baselines in terms of NMI and ARI. Additional ablation and hyperparameter studies further verify the effectiveness of the proposed motif-aware masking and reconstruction design.</p>