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In view of this, we examine the relationship between the missing higher-order structures and the instability, and find a positive correlation between higher-order loss ratio (HOLR) and temporal smoothing normalized mutual information (TSNMI). Based on this finding, we propose a new-brand higher-order edge enhancement (HOEE) algorithm, aiming to effectively reconstruct higher-order interactions to overcome the instability issue. The HOEE algorithm employs the higher-order activity potential (HAP) of nodes between consecutive snapshots to recover the loss of higher-order information by the transformation of the triangle motif, thus ensuring the temporal stability of dynamic communities. Experimental evaluation on synthetic and real-world dynamic networks demonstrates that HOEE outperforms state-of-the-art methods in community detection accuracy and significantly reduces community instability. Theoretical analysis confirms stability guarantees and characterizes graph property changes induced by HOEE. The HOEE algorithm effectively enhances temporal community stability through higher-order interaction reconstruction, providing a robust solution for dynamic network analysis.<\/jats:p>","DOI":"10.2478\/jaiscr-2026-0008","type":"journal-article","created":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T17:08:48Z","timestamp":1771002528000},"page":"145-162","source":"Crossref","is-referenced-by-count":0,"title":["Community Detection with Higher-Order Edge Enhancement in Temporal Networks"],"prefix":"10.2478","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-8007-2199","authenticated-orcid":false,"given":"Feiyu","family":"Yin","sequence":"first","affiliation":[{"name":"College of Biomedical Engineering , Fudan university , Shanghai , , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-7271-5386","authenticated-orcid":false,"given":"Yu","family":"Xia","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Mechanical System and Vibration , Shanghai Jiao Tong University , Shanghai , , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2562-236X","authenticated-orcid":false,"given":"Agnieszka","family":"Siwocha","sequence":"additional","affiliation":[{"name":"Information Technology Institute , SAN University , \u0141\u00f3d\u017a , Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-0066-3070","authenticated-orcid":false,"given":"Zhanyu","family":"Cen","sequence":"additional","affiliation":[{"name":"Ningbo Zhongda Leader Intelligent Transmission Co., Ltd ., Ningbo , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7335-995X","authenticated-orcid":false,"given":"Jie","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Electronic Engineering , National University of Defense Technology , Changsha , , China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"374","published-online":{"date-parts":[[2026,2,9]]},"reference":[{"key":"2026051423072711738_j_jaiscr-2026-0008_ref_001","doi-asserted-by":"crossref","unstructured":"J. Zhao and K. H. Cheong, Mase: Multi-attribute source estimator for epidemic transmission in complex networks, IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 54, no. 6, pp. 3308\u20133320, 2024.","DOI":"10.1109\/TSMC.2024.3349537"},{"key":"2026051423072711738_j_jaiscr-2026-0008_ref_002","doi-asserted-by":"crossref","unstructured":"T. P. Peixoto, Network reconstruction via the minimum description length principle, Physical Review X, vol. 15, no. 1, p. 011065, 2025.","DOI":"10.1103\/PhysRevX.15.011065"},{"key":"2026051423072711738_j_jaiscr-2026-0008_ref_003","doi-asserted-by":"crossref","unstructured":"J. Qian, W. Tong, M. Ling, X. Du, and H. Ding, Pixel-based clustering for local interpretable model-agnostic explanations, Journal of Artificial Intelligence and Soft Computing Research, vol. 15, no. 3, pp. 257\u2013277, 2025.","DOI":"10.2478\/jaiscr-2025-0013"},{"key":"2026051423072711738_j_jaiscr-2026-0008_ref_004","doi-asserted-by":"crossref","unstructured":"V. D. Blondel, J.-L. Guillaume, R. Lambiotte, and E. Lefebvre, Fast unfolding of communities in large networks, Journal of statistical mechanics: theory and experiment, vol. 2008, no. 10, p. P10008, 2008.","DOI":"10.1088\/1742-5468\/2008\/10\/P10008"},{"key":"2026051423072711738_j_jaiscr-2026-0008_ref_005","doi-asserted-by":"crossref","unstructured":"P. W. Holland, K. B. Laskey, and S. Leinhardt, Stochastic blockmodels: First steps, Social networks, vol. 5, no. 2, pp. 109\u2013137, 1983.","DOI":"10.1016\/0378-8733(83)90021-7"},{"key":"2026051423072711738_j_jaiscr-2026-0008_ref_006","doi-asserted-by":"crossref","unstructured":"M. A. K\u0142opotek, B. Starosta, and S. T. Wierzcho\u0144, Eigenvalue-based incremental spectral clustering, Journal of Artificial Intelligence and Soft Computing Research, vol. 14, no. 2, pp. 157\u2013169, 2024.","DOI":"10.2478\/jaiscr-2024-0009"},{"key":"2026051423072711738_j_jaiscr-2026-0008_ref_007","doi-asserted-by":"crossref","unstructured":"L. Ni, Q. Li, Y. Zhang, W. Luo, and V. S. Sheng, Lsaden: local spatial-aware community detection in evolving geo-social networks, IEEE Transactions on Knowledge and Data Engineering, vol. 36, no. 7, pp. 3265\u20133280, 2024.","DOI":"10.1109\/TKDE.2023.3348975"},{"key":"2026051423072711738_j_jaiscr-2026-0008_ref_008","doi-asserted-by":"crossref","unstructured":"P. Jiao, X. Zhang, Z. Liu, L. Zhang, H. Wu, M. Gao, T. Li, and J. Wu, A deep contrastive framework for unsupervised temporal link prediction in dynamic networks, Information Sciences, vol. 667, p. 120499, 2024.","DOI":"10.1016\/j.ins.2024.120499"},{"key":"2026051423072711738_j_jaiscr-2026-0008_ref_009","doi-asserted-by":"crossref","unstructured":"M. Zalasi\u0144ski, A. Cader, Z. Patora-Wysocka, and M. Xiao, Evaluating neural network models for predicting dynamic signature signals, Journal of Artificial Intelligence and Soft Computing Research, vol. 14, 2024.","DOI":"10.2478\/jaiscr-2024-0019"},{"key":"2026051423072711738_j_jaiscr-2026-0008_ref_010","doi-asserted-by":"crossref","unstructured":"C.-C. Hsu, L. Hsu-Chao, G.-Y. Jhang, J.-L. Huang, and W. Jun-Zhe, Nest: A novel ensemble method for estimating spatio-temporal gait parameters using inertial measurement units, Journal of Artificial Intelligence and Soft Computing Research, vol. 15, no. 4, pp. 319\u2013336, 2025.","DOI":"10.2478\/jaiscr-2025-0016"},{"key":"2026051423072711738_j_jaiscr-2026-0008_ref_011","unstructured":"T. Aynaud and J.-L. Guillaume, Static community detection algorithms for evolving networks, in 8th International Symposium on Modeling and Optimization In Mobile, Ad Hoc, and Wireless Networks. IEEE, 2010, pp. 513\u2013519."},{"key":"2026051423072711738_j_jaiscr-2026-0008_ref_012","doi-asserted-by":"crossref","unstructured":"M. J. Bommarito II, D. M. Katz, and J. L. Zelner, On the stability of community detection algorithms on longitudinal citation data, Procedia-Social and Behavioral Sciences, vol. 4, pp. 26\u201337, 2010.","DOI":"10.1016\/j.sbspro.2010.07.480"},{"key":"2026051423072711738_j_jaiscr-2026-0008_ref_013","doi-asserted-by":"crossref","unstructured":"L. Zhang, C. Qin, H. Yang, Z. Xiong, R. Cao, and F. Cheng, A diversified population migration-based multiobjective evolutionary algorithm for dynamic community detection, IEEE Transactions on Emerging Topics in Computational Intelligence, 2024.","DOI":"10.1109\/TETCI.2024.3451566"},{"key":"2026051423072711738_j_jaiscr-2026-0008_ref_014","doi-asserted-by":"crossref","unstructured":"J. Zhao, L. Zhou, C. Liu, Y. Jiang, S. Kwong, and Z.-H. Zhan, Multimodule-based dynamic community detection for enhancing innovation performance in crowdsourcing contests, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2025.","DOI":"10.1109\/TSMC.2025.3603622"},{"key":"2026051423072711738_j_jaiscr-2026-0008_ref_015","doi-asserted-by":"crossref","unstructured":"Y.-R. Lin, Y. Chi, S. Zhu, H. Sundaram, and B. L. Tseng, Facetnet: a framework for analyzing communities and their evolutions in dynamic networks, in Proceedings of the 17th international conference on World Wide Web, 2008, pp. 685\u2013694.","DOI":"10.1145\/1367497.1367590"},{"key":"2026051423072711738_j_jaiscr-2026-0008_ref_016","doi-asserted-by":"crossref","unstructured":"X. Zeng, W. Wang, C. Chen, and G. G. Yen, A consensus community-based particle swarm optimization for dynamic community detection, IEEE transactions on cybernetics, vol. 50, no. 6, pp. 2502\u20132513, 2019.","DOI":"10.1109\/TCYB.2019.2938895"},{"key":"2026051423072711738_j_jaiscr-2026-0008_ref_017","doi-asserted-by":"crossref","unstructured":"S. Krishnagopal and G. Bianconi, Spectral detection of simplicial communities via hodge laplacians, Physical Review E, vol. 104, no. 6, p. 064303, 2021.","DOI":"10.1103\/PhysRevE.104.064303"},{"key":"2026051423072711738_j_jaiscr-2026-0008_ref_018","doi-asserted-by":"crossref","unstructured":"X. Wu, C.-D. Wang, J.-Q. Lin, W.-D. Xi, and P. S. Yu, Motif-based contrastive learning for community detection, IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 9, pp. 11 706\u201311 719, 2024.","DOI":"10.1109\/TNNLS.2024.3367873"},{"key":"2026051423072711738_j_jaiscr-2026-0008_ref_019","doi-asserted-by":"crossref","unstructured":"M. Chen, S. Wang, M. Leng, Y. Chen, and Y. You, Core triangle motif-driven local community detection algorithm, IEEE Transactions on Computational Social Systems, pp. 1\u201312, 2025.","DOI":"10.1109\/TCSS.2025.3565926"},{"key":"2026051423072711738_j_jaiscr-2026-0008_ref_020","doi-asserted-by":"crossref","unstructured":"P.-Z. Li, L. Huang, C.-D. Wang, and J.-H. Lai, Edmot: An edge enhancement approach for motif-aware community detection, in Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, 2019, pp. 479\u2013487.","DOI":"10.1145\/3292500.3330882"},{"key":"2026051423072711738_j_jaiscr-2026-0008_ref_021","doi-asserted-by":"crossref","unstructured":"P.-Z. Li, L. Huang, C.-D. Wang, J.-H. Lai, and D. Huang, Community detection by motif-aware label propagation, ACM Transactions on Knowledge Discovery from Data (TKDD), vol. 14, no. 2, pp. 1\u201319, 2020.","DOI":"10.1145\/3378537"},{"key":"2026051423072711738_j_jaiscr-2026-0008_ref_022","doi-asserted-by":"crossref","unstructured":"J. Xiao, Y.-W. Wei, J. Cao, and X.-K. Xu, Higher-order fuzzy membership in motif modularity optimization, IEEE Transactions on Fuzzy Systems, vol. 32, no. 12, pp. 7143\u20137156, 2024.","DOI":"10.1109\/TFUZZ.2024.3482717"},{"key":"2026051423072711738_j_jaiscr-2026-0008_ref_023","doi-asserted-by":"crossref","unstructured":"W. Wang and X. Li, Temporal stable community in time-varying networks, IEEE Transactions on Network Science and Engineering, vol. 7, no. 3, pp. 1508\u20131520, 2019.","DOI":"10.1109\/TNSE.2019.2936865"},{"key":"2026051423072711738_j_jaiscr-2026-0008_ref_024","doi-asserted-by":"crossref","unstructured":"A. Bovet, J.-C. Delvenne, and R. Lambiotte, Flow stability for dynamic community detection, Science advances, vol. 8, no. 19, p. eabj3063, 2022.","DOI":"10.1126\/sciadv.abj3063"},{"key":"2026051423072711738_j_jaiscr-2026-0008_ref_025","doi-asserted-by":"crossref","unstructured":"Y. Ai, X. Xie, and X. Ma, Graph contrastive learning for tracking dynamic communities in temporal networks, IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 8, no. 5, pp. 3422\u20133435, 2024.","DOI":"10.1109\/TETCI.2024.3386844"},{"key":"2026051423072711738_j_jaiscr-2026-0008_ref_026","doi-asserted-by":"crossref","unstructured":"L. Yu, P. Li, J. Zhang, and J. Kurths, Dynamic community discovery via common subspace projection, New Journal of Physics, vol. 23, no. 3, p. 033029, 2021.","DOI":"10.1088\/1367-2630\/abe504"},{"key":"2026051423072711738_j_jaiscr-2026-0008_ref_027","doi-asserted-by":"crossref","unstructured":"G. Rossetti and R. Cazabet, Community discovery in dynamic networks: a survey, ACM computing surveys (CSUR), vol. 51, no. 2, pp. 1\u201337, 2018.","DOI":"10.1145\/3172867"},{"key":"2026051423072711738_j_jaiscr-2026-0008_ref_028","doi-asserted-by":"crossref","unstructured":"D. Zhuang, J. M. Chang, and M. Li, Dynamo: Dynamic community detection by incrementally maximizing modularity, IEEE Transactions on Knowledge and Data Engineering, vol. 33, no. 5, pp. 1934\u20131945, 2019.","DOI":"10.1109\/TKDE.2019.2951419"},{"key":"2026051423072711738_j_jaiscr-2026-0008_ref_029","doi-asserted-by":"crossref","unstructured":"J. Jiang, S. Yao, Y. Chen, B. He, Y. Niu, Y. Li, S. Sun, and Y. Liu, Community detection in heterogeneous information networks without materialization, Proceedings of the ACM on Management of Data, vol. 3, no. 3, pp. 1\u201327, 2025.","DOI":"10.1145\/3725276"},{"key":"2026051423072711738_j_jaiscr-2026-0008_ref_030","doi-asserted-by":"crossref","unstructured":"M. Seifikar, S. Farzi, and M. Barati, C-blondel: an efficient louvain-based dynamic community detection algorithm, IEEE Transactions on Computational Social Systems, vol. 7, no. 2, pp. 308\u2013318, 2020.","DOI":"10.1109\/TCSS.2020.2964197"},{"key":"2026051423072711738_j_jaiscr-2026-0008_ref_031","doi-asserted-by":"crossref","unstructured":"F. Liu, D. Choi, L. Xie, and K. Roeder, Global spectral clustering in dynamic networks, Proceedings of the National Academy of Sciences, vol. 115, no. 5, pp. 927\u2013932, 2018.","DOI":"10.1073\/pnas.1718449115"},{"key":"2026051423072711738_j_jaiscr-2026-0008_ref_032","doi-asserted-by":"crossref","unstructured":"A. Failla, R. Cazabet, G. Rossetti, and S. Citraro, Describing group evolution in temporal data using multi-faceted events, Machine Learning, vol. 113, no. 10, pp. 7591\u20137615, 2024.","DOI":"10.1007\/s10994-024-06600-4"},{"key":"2026051423072711738_j_jaiscr-2026-0008_ref_033","doi-asserted-by":"crossref","unstructured":"N. \u02d9Ilhan and S\u00b8. G.\u00d6\u011f\u00fcd\u00fcc\u00fc, Predicting community evolution based on time series modeling, in 2015 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). IEEE, 2015, pp. 1509\u20131516.","DOI":"10.1145\/2808797.2808913"},{"key":"2026051423072711738_j_jaiscr-2026-0008_ref_034","doi-asserted-by":"crossref","unstructured":"Z. Dhouioui and J. Akaichi, Tracking dynamic community evolution in social networks, in 2014 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014). IEEE, 2014, pp. 764\u2013770.","DOI":"10.1109\/ASONAM.2014.6921672"},{"key":"2026051423072711738_j_jaiscr-2026-0008_ref_035","doi-asserted-by":"crossref","unstructured":"Y. Chi, X. Song, D. Zhou, K. Hino, and B. L. Tseng, On evolutionary spectral clustering, ACM Transactions on Knowledge Discovery from Data (TKDD), vol. 3, no. 4, pp. 1\u201330, 2009.","DOI":"10.1145\/1631162.1631165"},{"key":"2026051423072711738_j_jaiscr-2026-0008_ref_036","doi-asserted-by":"crossref","unstructured":"F. Folino and C. Pizzuti, An evolutionary multiobjective approach for community discovery in dynamic networks, IEEE Transactions on Knowledge and Data Engineering, vol. 26, no. 8, pp. 1838\u20131852, 2013.","DOI":"10.1109\/TKDE.2013.131"},{"key":"2026051423072711738_j_jaiscr-2026-0008_ref_037","doi-asserted-by":"crossref","unstructured":"P. Wang, L. Gao, and X. Ma, Dynamic community detection based on network structural perturbation and topological similarity, Journal of Statistical Mechanics: Theory and Experiment, vol. 2017, no. 1, p. 013401, 2017.","DOI":"10.1088\/1742-5468\/2017\/1\/013401"},{"key":"2026051423072711738_j_jaiscr-2026-0008_ref_038","doi-asserted-by":"crossref","unstructured":"M. Rosvall and C. T. Bergstrom, Mapping change in large networks, PloS one, vol. 5, no. 1, p. e8694, 2010.","DOI":"10.1371\/journal.pone.0008694"},{"key":"2026051423072711738_j_jaiscr-2026-0008_ref_039","doi-asserted-by":"crossref","unstructured":"M. E. Newman and M. Girvan, Finding and evaluating community structure in networks, Physical review E, vol. 69, no. 2, p. 026113, 2004.","DOI":"10.1103\/PhysRevE.69.026113"},{"key":"2026051423072711738_j_jaiscr-2026-0008_ref_040","doi-asserted-by":"crossref","unstructured":"J. Leskovec, J. Kleinberg, and C. Faloutsos, Graph evolution: Densification and shrinking diameters, ACM transactions on Knowledge Discovery from Data (TKDD), vol. 1, no. 1, pp. 2\u2013es, 2007.","DOI":"10.1145\/1217299.1217301"},{"key":"2026051423072711738_j_jaiscr-2026-0008_ref_041","doi-asserted-by":"crossref","unstructured":"A. Badalyan, N. Ruggeri, and C. De Bacco, Structure and inference in hypergraphs with node attributes, Nature Communications, vol. 15, no. 1, p. 7073, 2024.","DOI":"10.1038\/s41467-024-51388-5"},{"key":"2026051423072711738_j_jaiscr-2026-0008_ref_042","unstructured":"H. Qin, R.-H. Li, G. Wang, X. Huang, Y. Yuan, and J. X. Yu, Mining stable communities in temporal networks by density-based clustering, IEEE Transactions on Big Data, 2020."},{"key":"2026051423072711738_j_jaiscr-2026-0008_ref_043","doi-asserted-by":"crossref","unstructured":"A. Lancichinetti, S. Fortunato, and F. Radicchi, Benchmark graphs for testing community detection algorithms, Physical review E, vol. 78, no. 4, p. 046110, 2008.","DOI":"10.1103\/PhysRevE.78.046110"},{"key":"2026051423072711738_j_jaiscr-2026-0008_ref_044","doi-asserted-by":"crossref","unstructured":"M. E. Newman, Modularity and community structure in networks, Proceedings of the national academy of sciences, vol. 103, no. 23, pp. 8577\u20138582, 2006.","DOI":"10.1073\/pnas.0601602103"},{"key":"2026051423072711738_j_jaiscr-2026-0008_ref_045","doi-asserted-by":"crossref","unstructured":"W. M. Rand, Objective criteria for the evaluation of clustering methods, Journal of the American Statistical association, vol. 66, no. 336, pp. 846\u2013850, 1971.","DOI":"10.1080\/01621459.1971.10482356"},{"key":"2026051423072711738_j_jaiscr-2026-0008_ref_046","doi-asserted-by":"crossref","unstructured":"A. R. Benson, D. F. Gleich, and J. Leskovec, Higher-order organization of complex networks, Science, vol. 353, no. 6295, pp. 163\u2013166, 2016.","DOI":"10.1126\/science.aad9029"},{"key":"2026051423072711738_j_jaiscr-2026-0008_ref_047","doi-asserted-by":"crossref","unstructured":"L. Huang, C.-D. Wang, and H.-Y. Chao, Higher-order multi-layer community detection, in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, no. 01, 2019, pp. 9945\u20139946.","DOI":"10.1609\/aaai.v33i01.33019945"},{"key":"2026051423072711738_j_jaiscr-2026-0008_ref_048","doi-asserted-by":"crossref","unstructured":"C. E. Tsourakakis, J. Pachocki, and M. Mitzenmacher, Scalable motif-aware graph clustering, in Proceedings of the 26th International Conference on World Wide Web, 2017, pp. 1451\u20131460.","DOI":"10.1145\/3038912.3052653"},{"key":"2026051423072711738_j_jaiscr-2026-0008_ref_049","doi-asserted-by":"crossref","unstructured":"R. Milo, S. Shen-Orr, S. Itzkovitz, N. Kashtan, D. Chklovskii, and U. Alon, Network motifs: simple building blocks of complex networks, Science, vol. 298, no. 5594, pp. 824\u2013827, 2002.","DOI":"10.1126\/science.298.5594.824"},{"key":"2026051423072711738_j_jaiscr-2026-0008_ref_050","doi-asserted-by":"crossref","unstructured":"N. K. Ahmed, J. Neville, R. A. Rossi, and N. Duffield, Efficient graphlet counting for large networks, in 2015 IEEE International Conference on Data Mining. IEEE, 2015, pp. 1\u201310.","DOI":"10.1109\/ICDM.2015.141"},{"key":"2026051423072711738_j_jaiscr-2026-0008_ref_051","doi-asserted-by":"crossref","unstructured":"N. Pedreschi, D. Battaglia, and A. Barrat, The temporal rich club phenomenon, pp. 931\u2013938, 2022.","DOI":"10.1038\/s41567-022-01634-8"},{"key":"2026051423072711738_j_jaiscr-2026-0008_ref_052","doi-asserted-by":"crossref","unstructured":"P. Jiao, T. Li, H. Wu, C.-D. Wang, D. He, and W. Wang, Hb-dsbm: Modeling the dynamic complex networks from community level to node level, IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 11, pp. 8310\u20138323, 2022.","DOI":"10.1109\/TNNLS.2022.3149285"},{"key":"2026051423072711738_j_jaiscr-2026-0008_ref_053","doi-asserted-by":"crossref","unstructured":"E. K. Kao, S. T. Smith, and E. M. Airoldi, Hybrid mixed-membership blockmodel for inference on realistic network interactions, IEEE Transactions on Network Science and Engineering, vol. 6, no. 3, pp. 336\u2013350, 2018.","DOI":"10.1109\/TNSE.2018.2823324"},{"key":"2026051423072711738_j_jaiscr-2026-0008_ref_054","doi-asserted-by":"crossref","unstructured":"M. E. Newman, Assortative mixing in networks, Physical review letters, vol. 89, no. 20, p. 208701, 2002.","DOI":"10.1103\/PhysRevLett.89.208701"},{"key":"2026051423072711738_j_jaiscr-2026-0008_ref_055","doi-asserted-by":"crossref","unstructured":"H. Yin, A. R. Benson, and J. Leskovec, Higher-order clustering in networks, Physical Review E, vol. 97, no. 5, p. 052306, 2018.","DOI":"10.1103\/PhysRevE.97.052306"}],"container-title":["Journal of Artificial Intelligence and Soft Computing Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/reference-global.com\/pdf\/10.2478\/jaiscr-2026-0008","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T23:07:51Z","timestamp":1778800071000},"score":1,"resource":{"primary":{"URL":"https:\/\/reference-global.com\/article\/10.2478\/jaiscr-2026-0008"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,9]]},"references-count":55,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026,2,9]]},"published-print":{"date-parts":[[2026,3,1]]}},"alternative-id":["10.2478\/jaiscr-2026-0008"],"URL":"https:\/\/doi.org\/10.2478\/jaiscr-2026-0008","relation":{},"ISSN":["2449-6499"],"issn-type":[{"value":"2449-6499","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,9]]}}}