{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T14:37:07Z","timestamp":1781534227530,"version":"3.54.5"},"reference-count":40,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T00:00:00Z","timestamp":1778630400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"NSFC","doi-asserted-by":"crossref","award":["U25A20428"],"award-info":[{"award-number":["U25A20428"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"award":["U25A20428"],"award-info":[{"award-number":["U25A20428"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]},{"name":"Hunan Provincial Key Research and Development Program","award":["2024AQ2028"],"award-info":[{"award-number":["2024AQ2028"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>In the era of data-driven education, educational social networks generate large volumes of high-dimensional and complex-structured data through learner interactions, collaborative activities, and resource-sharing behaviors, posing significant challenges to traditional unsupervised learning methods. Such data often exhibit non-convex distributions, heterogeneity, and noise sensitivity, making conventional clustering approaches insufficient for capturing their intrinsic structural relationships. To address this issue, this paper proposes Quantum Fidelity-Based Graph K-Means (QGKM), a clustering framework for robust pattern recognition in educational social networks. Specifically, QGKM employs quantum state encoding to map complex educational data into a quantum state space and utilizes quantum fidelity as a similarity metric to uncover latent correlations that Euclidean distance cannot effectively capture. In addition, the incorporation of k-nearest neighbor graphs preserves the local geometric structure of learner interaction networks, while a deterministic greedy hierarchical merging strategy eliminates the instability caused by random initialization. Experimental results on seven real-world datasets demonstrate that QGKM consistently outperforms classical K-Means in clustering accuracy. The proposed framework provides an effective solution for learning pattern discovery, learner profiling, and intelligent recommendation in digital education environments.<\/jats:p>","DOI":"10.3390\/a19050386","type":"journal-article","created":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T16:26:37Z","timestamp":1778689597000},"page":"386","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["QGKM: A Quantum Fidelity-Based Graph Clustering Framework for Robust Data Pattern Recognition in Education Social Networks"],"prefix":"10.3390","volume":"19","author":[{"given":"Neal N.","family":"Xiong","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weiqing","family":"Long","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dacheng","family":"He","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3100-0006","authenticated-orcid":false,"given":"Xiangwei","family":"Meng","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zulong","family":"Diao","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8473-8077","authenticated-orcid":false,"given":"Sergey M.","family":"Avdoshin","sequence":"additional","affiliation":[{"name":"School of Computer Engineering, National Research University Higher School of Economics, Moscow 123458, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yevgeni","family":"Koucheryavy","sequence":"additional","affiliation":[{"name":"School of Computer Engineering, National Research University Higher School of Economics, Moscow 123458, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,5,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"14595","DOI":"10.1109\/JIOT.2021.3067904","article-title":"DRLR: A deep-reinforcement-learning-based recruitment scheme for massive data collections in 6G-based IoT networks","volume":"9","author":"Li","year":"2021","journal-title":"IEEE Internet Things J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1504\/IJSNET.2012.047720","article-title":"Distributed k-connected fault-tolerant topology control algorithms with PSO in future autonomic sensor systems","volume":"12","author":"Guo","year":"2012","journal-title":"Int. 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