{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T07:32:31Z","timestamp":1771659151074,"version":"3.50.1"},"reference-count":68,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2020,5,13]],"date-time":"2020-05-13T00:00:00Z","timestamp":1589328000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities of China","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Science Foundation of China","doi-asserted-by":"crossref","award":["61672417, 61876138, 61772091, and 61802035"],"award-info":[{"award-number":["61672417, 61876138, 61772091, and 61802035"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100017380","name":"Science and Technology Foundation of Shenzhen City","doi-asserted-by":"crossref","award":["JCYJ2017081-6100845994"],"award-info":[{"award-number":["JCYJ2017081-6100845994"]}],"id":[{"id":"10.13039\/100017380","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Knowl. Discov. Data"],"published-print":{"date-parts":[[2020,6,30]]},"abstract":"<jats:p>\n            Community detection aims to partition network nodes into a set of clusters, such that nodes are more densely connected to each other within the same cluster than other clusters. For attributed networks, apart from the denseness requirement of topology structure, the attributes of nodes in the same community should also be homogeneous. Network embedding has been proved extremely useful in a variety of tasks, such as node classification, link prediction, and graph visualization, but few works dedicated to unsupervised embedding of node features specified for clustering task, which is vital for community detection and graph clustering. By post-processing with clustering algorithms like\n            <jats:italic>k<\/jats:italic>\n            -means, most existing network embedding methods can be applied to clustering tasks. However, the learned embeddings are not designed for clustering task, they only learn topological and attributed information of networks, and no clustering-oriented information is explored. In this article, we propose an algorithm named Network Embedding for node Clustering (NEC) to learn network embedding for node clustering in attributed graphs. Specifically, the presented work introduces a framework that simultaneously learns graph structure-based representations and clustering-oriented representations together. The framework consists of the following three modules: graph convolutional autoencoder module, soft modularity maximization module, and self-clustering module. Graph convolutional autoencoder module learns node embeddings based on topological structure and node attributes. We introduce soft modularity, which can be easily optimized using gradient descent algorithms, to exploit the community structure of networks. By integrating clustering loss and embedding loss, NEC can jointly optimize node cluster labels assignment and learn representations that keep local structure of network. This model can be effectively optimized using stochastic gradient algorithm. Empirical experiments on real-world networks and synthetic networks validate the feasibility and effectiveness of our algorithm on community detection task compared with network embedding based methods and traditional community detection methods.\n          <\/jats:p>","DOI":"10.1145\/3385415","type":"journal-article","created":{"date-parts":[[2020,5,19]],"date-time":"2020-05-19T10:42:16Z","timestamp":1589884936000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":86,"title":["Network Embedding for Community Detection in Attributed Networks"],"prefix":"10.1145","volume":"14","author":[{"given":"Heli","family":"Sun","sequence":"first","affiliation":[{"name":"Xi\u2019an Jiaotong University, Xi\u2019an, Shanxi, China"}]},{"given":"Fang","family":"He","sequence":"additional","affiliation":[{"name":"Xi\u2019an Jiaotong University, Xi\u2019an, Shanxi, China"}]},{"given":"Jianbin","family":"Huang","sequence":"additional","affiliation":[{"name":"Xidian University, Xi\u2019an, Shanxi, China"}]},{"given":"Yizhou","family":"Sun","sequence":"additional","affiliation":[{"name":"University of California, Los Angeles, CA"}]},{"given":"Yang","family":"Li","sequence":"additional","affiliation":[{"name":"Xi\u2019an Jiaotong University, Xi\u2019an, Shanxi, China"}]},{"given":"Chenyu","family":"Wang","sequence":"additional","affiliation":[{"name":"Xi\u2019an Jiaotong University, Xi\u2019an, Shanxi, China"}]},{"given":"Liang","family":"He","sequence":"additional","affiliation":[{"name":"Xi\u2019an Jiaotong University, Xi\u2019an, Shanxi, China"}]},{"given":"Zhongbin","family":"Sun","sequence":"additional","affiliation":[{"name":"Xi\u2019an Jiaotong University, Xi\u2019an, Shanxi, China"}]},{"given":"Xiaolin","family":"Jia","sequence":"additional","affiliation":[{"name":"Xi\u2019an Jiaotong University, Xi\u2019an, Shanxi, China"}]}],"member":"320","published-online":{"date-parts":[[2020,5,13]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611972825.38"},{"key":"e_1_2_1_2_1","volume-title":"Proceedings of the 14th International Conference on Neural Information Processing Systems: Natural and Synthetic. 585--591","author":"Belkin Mikhail","year":"2002"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2018.10.045"},{"key":"e_1_2_1_4_1","volume-title":"Information Science and Statistics","author":"Bishop Christopher M."},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1088\/1742-5468\/2008\/10\/P10008"},{"key":"e_1_2_1_6_1","volume-title":"Proceedings of the 32nd AAAI Conference on Artificial Intelligence.","author":"Bojchevski Aleksandar","year":"2018"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2010.231"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/2806416.2806512"},{"key":"e_1_2_1_9_1","volume-title":"Proceedings of the 13th AAAI Conference on Artificial Intelligence. 1145--1152","author":"Cao Shaosheng","year":"2016"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3132847.3132925"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.626"},{"key":"e_1_2_1_12_1","doi-asserted-by":"crossref","DOI":"10.1145\/1921632.1921638","volume-title":"Clustering large attributed graphs: A balance between structural and attribute similarities. ACM Transactions on Knowledge Discovery from Data 5, 2","author":"Cheng Hong","year":"2011"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11222-007-9046-7"},{"key":"e_1_2_1_14_1","volume-title":"Proceedings of the IEEE 29th International Conference on Tools with Artificial Intelligence.","author":"Di Jin","year":"2017"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.physrep.2009.11.002"},{"key":"e_1_2_1_16_1","doi-asserted-by":"crossref","unstructured":"Anna Goldenberg Alice X. Zheng Stephen E. Fienberg Edoardo M. Airoldi etal 2010. A survey of statistical network models. Foundations and Trends\u00ae in Machine Learning 2 2 (2010) 129--233.  Anna Goldenberg Alice X. Zheng Stephen E. Fienberg Edoardo M. Airoldi et al. 2010. A survey of statistical network models. Foundations and Trends\u00ae in Machine Learning 2 2 (2010) 129--233.","DOI":"10.1561\/2200000005"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939754"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/2996197"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2017\/243"},{"key":"e_1_2_1_20_1","volume-title":"Proceedings of the 31st International Conference on Neural Information Processing Systems. 1025--1035","author":"Hamilton Will","year":"2017"},{"key":"e_1_2_1_21_1","volume-title":"Proceedings of the 29th AAAI Conference on Artificial Intelligence.","author":"He Dongxiao","year":"2015"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1016\/0378-8733(83)90021-7"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/3041021.3051158"},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.5555\/3305381.3305542"},{"key":"e_1_2_1_25_1","volume-title":"Proceedings of the 29th AAAI Conference on Artificial Intelligence.","author":"Jin Di","year":"2015"},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.83.016107"},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2019.102054"},{"key":"e_1_2_1_28_1","volume-title":"Procededings of the 3rd International Conference on Learning Representations (ICLR\u201915)","author":"Diederik"},{"key":"e_1_2_1_29_1","volume-title":"Kipf and Max Welling","author":"Thomas","year":"2016"},{"key":"e_1_2_1_30_1","volume-title":"Kipf and Max Welling","author":"Thomas","year":"2016"},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.78.046110"},{"key":"e_1_2_1_32_1","first-page":"2579","article-title":"Visualizing data using t-SNE","author":"van der Maaten Laurens","year":"2008","journal-title":"Journal of Machine Learning Research 9"},{"key":"e_1_2_1_33_1","volume-title":"Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781","author":"Mikolov Tomas","year":"2013"},{"key":"e_1_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2855437"},{"key":"e_1_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.0601602103"},{"key":"e_1_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1038\/ncomms11863"},{"key":"e_1_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.69.026113"},{"key":"e_1_2_1_38_1","volume-title":"Proceedings of the 14th International Conference on Neural Information Processing Systems. 849--856","author":"Ng Andrew Y.","year":"2002"},{"key":"e_1_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/354756.354805"},{"key":"e_1_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1007\/s13278-015-0262-4"},{"key":"e_1_2_1_41_1","unstructured":"Adam Paszke Sam Gross Soumith Chintala Gregory Chanan Edward Yang Zachary DeVito Zeming Lin Alban Desmaison Luca Antiga and Adam Lerer. 2017. Automatic differentiation in PyTorch. In NIPS-W.  Adam Paszke Sam Gross Soumith Chintala Gregory Chanan Edward Yang Zachary DeVito Zeming Lin Alban Desmaison Luca Antiga and Adam Lerer. 2017. Automatic differentiation in PyTorch. In NIPS-W."},{"key":"e_1_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623732"},{"key":"e_1_2_1_43_1","volume-title":"Plummer and L\u00e1szl\u00f3 Lov\u00e1sz","author":"Michael","year":"1986"},{"key":"e_1_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.83.066114"},{"key":"e_1_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.76.036106"},{"key":"e_1_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.0706851105"},{"key":"e_1_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.290.5500.2323"},{"key":"e_1_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1111\/coin.12178"},{"key":"e_1_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/2736277.2741093"},{"key":"e_1_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.290.5500.2319"},{"key":"e_1_2_1_51_1","volume-title":"Proceedings of the 28th AAAI Conference on Artificial Intelligence. 1293--1299","author":"Tian Fei","year":"2014"},{"key":"e_1_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11222-007-9033-z"},{"key":"e_1_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939753"},{"key":"e_1_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2013.06.038"},{"key":"e_1_2_1_55_1","volume-title":"Proceedings of the International Conference on Machine Learning. 478--487","author":"Xie Junyuan","year":"2016"},{"key":"e_1_2_1_56_1","volume-title":"Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 824--833","author":"Xu Xiaowei"},{"key":"e_1_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-017-1030-8"},{"key":"e_1_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1145\/2213836.2213894"},{"key":"e_1_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.1145\/2629616"},{"key":"e_1_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.5555\/2832415.2832542"},{"key":"e_1_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.1145\/2433396.2433471"},{"key":"e_1_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2013.167"},{"key":"e_1_2_1_63_1","volume-title":"Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. 5147--5156","author":"Yang J.","year":"2016"},{"key":"e_1_2_1_64_1","volume-title":"Proceedings of the 25th International Joint Conference on Artificial Intelligence. 2252--2258","author":"Yang Liang","year":"2016"},{"key":"e_1_2_1_65_1","doi-asserted-by":"publisher","DOI":"10.1145\/2556195.2556259"},{"key":"e_1_2_1_66_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2010.01.026"},{"key":"e_1_2_1_67_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939673"},{"key":"e_1_2_1_68_1","doi-asserted-by":"publisher","DOI":"10.14778\/1687627.1687709"}],"container-title":["ACM Transactions on Knowledge Discovery from Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3385415","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3385415","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T22:38:50Z","timestamp":1750199930000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3385415"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,5,13]]},"references-count":68,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2020,6,30]]}},"alternative-id":["10.1145\/3385415"],"URL":"https:\/\/doi.org\/10.1145\/3385415","relation":{},"ISSN":["1556-4681","1556-472X"],"issn-type":[{"value":"1556-4681","type":"print"},{"value":"1556-472X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,5,13]]},"assertion":[{"value":"2019-08-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2020-02-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2020-05-13","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}