{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T21:37:53Z","timestamp":1773524273693,"version":"3.50.1"},"reference-count":26,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2020,6,3]],"date-time":"2020-06-03T00:00:00Z","timestamp":1591142400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Scientific Research Foundation for Advanced Talents of Jiangsu University","award":["14JDG040"],"award-info":[{"award-number":["14JDG040"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>In this paper, we proposed a novel community detection method based on the network structure transformation, that utilized deep learning. The probability transfer matrix of the network adjacency matrix was calculated, and the probability transfer matrix was used as the input of the deep learning network. We use a denoising autoencoder to nonlinearly map the probability transfer matrix into a new sub space. The community detection was calculated with the deep learning nonlinear transform of the network structure. The network nodes were clustered in the new space with the K-means clustering algorithm. The division of the community structure was obtained. We conducted extensive experimental tests on the benchmark networks and the standard networks (known as the initial division of communities). We tested the clustering results of the different types, and compared with the three base algorithms. The results showed that the proposed community detection model was effective. We compared the results with other traditional community detection methods. The empirical results on datasets of varying sizes demonstrated that our proposed method outperformed the other community detection methods for this task.<\/jats:p>","DOI":"10.3390\/sym12060944","type":"journal-article","created":{"date-parts":[[2020,6,5]],"date-time":"2020-06-05T03:32:21Z","timestamp":1591327941000},"page":"944","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Network Structural Transformation-Based Community Detection with Autoencoder"],"prefix":"10.3390","volume":"12","author":[{"given":"Xia","family":"Geng","sequence":"first","affiliation":[{"name":"School of Computer Science and Communication Engineering, Jiangsu University, Jiangsu 212013, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0350-4055","authenticated-orcid":false,"given":"Hu","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Communication Engineering, Jiangsu University, Jiangsu 212013, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Electrical and Information Engineering, Jiangsu University, Jiangsu 212013, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,6,3]]},"reference":[{"key":"ref_1","unstructured":"Newman, M.E.J., Barabasi, A.-L., and Watts, D.J. (2006). The Structure and Dynamics of Networks, Princeton University Press."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"440","DOI":"10.1038\/30918","article-title":"Collective dynamics of \u201csmall-world\u201d networks","volume":"393","author":"Watts","year":"1998","journal-title":"Nature"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1126\/science.286.5439.509","article-title":"Emergence of scaling in random networks","volume":"286","author":"Albert","year":"1999","journal-title":"Science"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"7821","DOI":"10.1073\/pnas.122653799","article-title":"Community structure in social and biological networks","volume":"99","author":"Girvan","year":"2002","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_5","unstructured":"Hu, L., Xinghao, H., and Yuqing, S. (2015, January 22\u201325). Partitioning the Firing Patterns of Spike Trains by Community Modularity. Proceedings of the 37th Cognitive Science Society Annual Conference, Pasadena, CA, USA."},{"key":"ref_6","unstructured":"Hu, L., Zhe, L., Yuqing, S., and Hui, W. (2014, January 6\u201311). Hierarchical organization in neuronal functional networks during working memory tasks. Proceedings of the 2014 International Joint Conference on Neural Networks (IJCNN), Beijing, China."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"026113","DOI":"10.1103\/PhysRevE.69.026113","article-title":"Finding and evaluating community structure in networks","volume":"69","author":"Newman","year":"2004","journal-title":"Phys. Rev. E"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"801","DOI":"10.1109\/TKDE.2015.2496345","article-title":"Subspace based network community detection using sparse linear coding","volume":"28","author":"Mahmood","year":"2016","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_9","unstructured":"Li, W., and Schuurmans, D. (2011, January 16\u201322). Modular community detection in networks. Proceedings of the IJCAI Proceedings-International Joint Conference on Artificial Intelligence, Barcelona, Catalonia, Spain."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"016104","DOI":"10.1103\/PhysRevE.77.016104","article-title":"Identifying network communities with a high resolution","volume":"77","author":"Ruan","year":"2008","journal-title":"Phys. Rev. E"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1073\/pnas.0605965104","article-title":"Resolution limit in community detection","volume":"104","author":"Fortunato","year":"2007","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"6156","DOI":"10.1016\/j.physa.2012.06.062","article-title":"Detection of community structure in networks based on community coefficients","volume":"391","author":"Hu","year":"2012","journal-title":"Phys. A Stat. Mech. Appl."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"066114","DOI":"10.1103\/PhysRevE.83.066114","article-title":"Overlapping community detection using bayesian non-negative matrix factorization","volume":"83","author":"Psorakis","year":"2011","journal-title":"Phys. Rev. E"},{"key":"ref_14","unstructured":"Aicher, C., Jacobs, A.Z., and Clauset, A. (2013). Adapting the stochastic block model to edge-weighted networks. arXiv."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Langone, R., Mall, R., Vandewalle, J., and Suykens, J.A.K. (2016). Discovering Cluster Dynamics Using Kernel Spectral Methods\/\/Complex Systems and Networks, Springer.","DOI":"10.1007\/978-3-662-47824-0_1"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Tian, F., Gao, B., Cui, Q., Chen, E., and Liu, T.-Y. (2014, January 27\u201331). Learning Deep Representations for Graph Clustering. Proceedings of the AAAI, Qu\u00e9bec City, QC, Canada.","DOI":"10.1609\/aaai.v28i1.8916"},{"key":"ref_17","unstructured":"Yang, L., Cao, X., He, D., Wang, C., Wang, X., and Zhang, W. (2016, January 9\u201315). Modularity Based Community Detection with Deep Learning. Proceedings of the IJCAI, New York, NY, USA."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Cao, S., Lu, W., and Xu, Q. (2016, January 12\u201317). Deep Neural Networks for Learning Graph Representations. Proceedings of the AAAI, Phoenix, AZ, USA.","DOI":"10.1609\/aaai.v30i1.10179"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1561\/2200000006","article-title":"Learning deep architectures for AI","volume":"2","author":"Bengio","year":"2009","journal-title":"Found. Trends Mach. Learn."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"LeCun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"046110","DOI":"10.1103\/PhysRevE.78.046110","article-title":"Benchmark graphs for testing community detection algorithms","volume":"78","author":"Andrea","year":"2008","journal-title":"Phys. Rev. E"},{"key":"ref_22","unstructured":"Le Martelot, E., and Hankin, C. (2011, January 26\u201329). Multi-scale Community Detection using Stability as Optimisation Criterion in a Greedy Algorithm. Proceedings of the KDIR, Paris, France."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Peng, X., Feng, J., Lu, J., Yau, W.Y., and Yi, Z. (2017, January 4\u20139). Cascade Subspace Clustering. Proceedings of the AAAI, San Francisco, CA, USA.","DOI":"10.1609\/aaai.v31i1.10824"},{"key":"ref_24","unstructured":"Hu, L., YuQing, S., and Hui, W. (2020). Multiple-Kernel Combination Fuzzy Clustering for Community Detection. Soft Comput."},{"key":"ref_25","unstructured":"Hu, L., Saixiong, L., Hui, W., and Juanjuan, T. (2020). Multi-kernel fuzzy clustering based on auto-encoder for fMRI functional network. Expert Syst. Appl."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"113079","DOI":"10.1016\/j.eswa.2019.113079","article-title":"Discriminative globality and locality preserving graph embedding for dimensionality reduction","volume":"144","author":"Jianping","year":"2020","journal-title":"Expert Syst. Appl."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/12\/6\/944\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:35:26Z","timestamp":1760175326000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/12\/6\/944"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,6,3]]},"references-count":26,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2020,6]]}},"alternative-id":["sym12060944"],"URL":"https:\/\/doi.org\/10.3390\/sym12060944","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,6,3]]}}}