{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T03:48:07Z","timestamp":1772164087687,"version":"3.50.1"},"reference-count":33,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science and Technology Project of State Grid Corporation of China","award":["5700-202440239A-1-1-ZN"],"award-info":[{"award-number":["5700-202440239A-1-1-ZN"]}]}],"content-domain":{"domain":["www.mdpi.com"],"crossmark-restriction":true},"short-container-title":["Symmetry"],"abstract":"<jats:p>Edge intelligence plays an increasingly vital role in ensuring the reliability of distributed microservice-based applications, which are widely used in domains such as e-commerce, industrial IoT, and cloud-edge collaborative platforms. However, anomaly detection in these systems encounters a critical challenge: labeled anomaly data are scarce. This scarcity leads to severe class asymmetry and compromised detection performance, particularly under the resource constraints of edge environments. Recent approaches based on Graph Neural Networks (GNNs)\u2014often integrated with DeepSVDD and regularization techniques\u2014have shown potential, but they rarely address this asymmetry in an adaptive, scenario-specific way. This work proposes Heterogeneous Graph Adaptive Augmentation (HGAA), a framework tailored for edge intelligence scenarios. HGAA dynamically optimizes graph data augmentation by leveraging feedback from online anomaly detection. To enhance detection accuracy while adhering to resource constraints, the framework incorporates a selective bias toward underrepresented anomaly types. It uses knowledge distillation to model dataset-dependent distributions and adaptively adjusts augmentation probabilities, thus avoiding excessive computational overhead in edge environments. Additionally, a dynamic adjustment mechanism evaluates augmentation success rates in real time, refining the selection processes to maintain model robustness. Experiments were conducted on two real-world datasets (TraceLog and FlowGraph) under simulated edge scenarios. Results show that HGAA consistently outperforms competitive baseline methods. Specifically, compared with the best non-adaptive augmentation strategies, HGAA achieves an average improvement of 4.5% in AUC and 4.6% in AP. Even larger gains are observed in challenging cases: for example, when using the HGT model on the TraceLog dataset, AUC improves by 14.6% and AP by 18.1%. Beyond accuracy, HGAA also significantly enhances efficiency: compared with filter-based methods, training time is reduced by up to 71% on TraceLog and 8.6% on FlowGraph, confirming its suitability for resource-constrained edge environments. These results highlight the potential of adaptive, edge-aware augmentation techniques in improving microservice anomaly detection within heterogeneous, resource-limited environments.<\/jats:p>","DOI":"10.3390\/sym17101623","type":"journal-article","created":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T07:58:13Z","timestamp":1759305493000},"page":"1623","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["HGAA: A Heterogeneous Graph Adaptive Augmentation Method for Asymmetric Datasets"],"prefix":"10.3390","volume":"17","author":[{"given":"Hongbo","family":"Zhao","sequence":"first","affiliation":[{"name":"Institute of Artificial Intelligence, Xiamen University, Xiamen 361005, China"}]},{"given":"Wei","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Informatics, Xiamen University, Xiamen 361005, China"},{"name":"NARI Group Corporation\/State Grid Electric Power Research Institute, Nanjing 211106, China"}]},{"given":"Congming","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Informatics, Xiamen University, Xiamen 361005, China"}]},{"given":"Weining","family":"Shi","sequence":"additional","affiliation":[{"name":"School of Informatics, Xiamen University, Xiamen 361005, China"}]},{"given":"Zhihong","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Informatics, Xiamen University, Xiamen 361005, China"}]},{"given":"Jianfei","family":"Chen","sequence":"additional","affiliation":[{"name":"State Grid Shandong Electric Power Company, Jinan 250000, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhang, C., Peng, X., Sha, C., Zhang, K., Fu, Z., Wu, X., Lin, Q., and Zhang, D. 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