{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T02:43:08Z","timestamp":1783737788093,"version":"3.55.0"},"reference-count":55,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,1,30]],"date-time":"2024-01-30T00:00:00Z","timestamp":1706572800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,30]],"date-time":"2024-01-30T00:00:00Z","timestamp":1706572800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J. Comput. Sci. Technol."],"published-print":{"date-parts":[[2024,2]]},"DOI":"10.1007\/s11390-021-1277-0","type":"journal-article","created":{"date-parts":[[2024,4,11]],"date-time":"2024-04-11T07:02:21Z","timestamp":1712818941000},"page":"192-210","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["SMEC: Scene Mining for E-Commerce"],"prefix":"10.1007","volume":"39","author":[{"given":"Gang","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiang","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zi-Yi","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Da-Wei","family":"Yin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuai","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,1,30]]},"reference":[{"key":"1277_CR1","doi-asserted-by":"publisher","unstructured":"Sarwar B, Karypis G, Konstan J, Riedl J. Item-based collaborative filtering recommendation algorithms. In Proc. the 10th International Conference on World Wide Web, May 2001, pp.285\u2013295. https:\/\/doi.org\/10.1145\/371920.372071.","DOI":"10.1145\/371920.372071"},{"key":"1277_CR2","doi-asserted-by":"publisher","unstructured":"Rendle S, Freudenthaler C, Schmidt-Thieme L. Factorizing personalized Markov chains for next-basket recommendation. In Proc. the 19th International Conference on World Wide Web, Apr. 2010, pp.811\u2013820. https:\/\/doi.org\/10.1145\/1772690.1772773.","DOI":"10.1145\/1772690.1772773"},{"key":"1277_CR3","doi-asserted-by":"publisher","unstructured":"Zhou G R, Zhu X Q, Song C R, Fan Y, Zhu H, Ma X, Yan Y H, Jin J Q, Li H, Gai K. Deep interest network for click-through rate prediction. In Proc. the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Jul. 2018, pp.1059\u20131068. https:\/\/doi.org\/10.1145\/3219819.3219823.","DOI":"10.1145\/3219819.3219823"},{"key":"1277_CR4","doi-asserted-by":"publisher","unstructured":"Kiapour M H, Yamaguchi K, Berg A C, Berg T L. Hipster wars: Discovering elements of fashion styles. In Proc. the 13th European Conference on Computer Vision, Sept. 2014, pp.472\u2013488. https:\/\/doi.org\/10.1007\/978-3-319-10590-1_31.","DOI":"10.1007\/978-3-319-10590-1_31"},{"key":"1277_CR5","doi-asserted-by":"publisher","unstructured":"Liu Z W, Luo P, Qiu S, Wang X G, Tang X O. Deep-Fashion: Powering robust clothes recognition and retrieval with rich annotations. In Proc. the 2016 IEEE Conference on Computer Vision and Pattern Recognition, Jun. 2016, pp.1096\u20131104. https:\/\/doi.org\/10.1109\/CVPR.2016.124.","DOI":"10.1109\/CVPR.2016.124"},{"key":"1277_CR6","doi-asserted-by":"publisher","unstructured":"Kang W C, Kim E, Leskovec J, Rosenberg C, McAuley J. Complete the look: Scene-based complementary product recommendation. In Proc. the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Jun. 2019, pp.10524\u201310533. https:\/\/doi.org\/10.1109\/CVPR.2019.01078.","DOI":"10.1109\/CVPR.2019.01078"},{"key":"1277_CR7","doi-asserted-by":"publisher","unstructured":"Sun Y Z, Han J W, Yan X F, Yu P S, Wu T Y. PathSim: Meta path-based top-K similarity search in heterogeneous information networks. Proceedings of the VLDB Endowment, 2011, 4(11): 992\u20131003. https:\/\/doi.org\/10.14778\/3402707.3402736.","DOI":"10.14778\/3402707.3402736"},{"key":"1277_CR8","doi-asserted-by":"publisher","unstructured":"Huang Z P, Zheng Y D, Cheng R, Sun Y Z, Mamoulis N, Li X. Meta structure: Computing relevance in large heterogeneous information networks. In Proc. the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Aug. 2016, pp.1595\u20131604. https:\/\/doi.org\/10.1145\/2939672.2939815.","DOI":"10.1145\/2939672.2939815"},{"key":"1277_CR9","doi-asserted-by":"publisher","unstructured":"Fang Y, Lin W Q, Zheng V W, Wu M, Chang K C C, Li X L. Semantic proximity search on graphs with metagraph-based learning. In Proc. the 32nd IEEE International Conference on Data Engineering, May 2016, pp.277\u2013288. https:\/\/doi.org\/10.1109\/ICDE.2016.7498247.","DOI":"10.1109\/ICDE.2016.7498247"},{"key":"1277_CR10","doi-asserted-by":"publisher","unstructured":"Zhao H, Yao Q M, Li J D, Song Y Q, Lee D L. Metagraph based recommendation fusion over heterogeneous information networks. In Proc. the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Aug. 2017, pp.635\u2013644. https:\/\/doi.org\/10.1145\/3097983.3098063.","DOI":"10.1145\/3097983.3098063"},{"key":"1277_CR11","doi-asserted-by":"publisher","unstructured":"Wang X, Ji H Y, Shi C, Wang B, Ye Y F, Cui P, Yu P S. Heterogeneous graph attention network. In Proc. the 28th World Wide Web Conference, May 2019, pp.2022\u20132032. https:\/\/doi.org\/10.1145\/3308558.3313562.","DOI":"10.1145\/3308558.3313562"},{"key":"1277_CR12","doi-asserted-by":"publisher","unstructured":"Liu J L, Wang C, Gao J, Han J W. Multi-view clustering via joint nonnegative matrix factorization. In Proc. the 13th SIAM International Conference on Data Mining, Dec. 2013, pp.252\u2013260. https:\/\/doi.org\/10.1137\/1.9781611972832.28.","DOI":"10.1137\/1.9781611972832."},{"key":"1277_CR13","doi-asserted-by":"publisher","unstructured":"Qiu J Z, Dong Y X, Ma H, Li J, Wang K S, Tang J. Network embedding as matrix factorization: Unifying Deep-Walk, LINE, PTE, and node2vec. In Proc. the 11th ACM International Conference on Web Search and Data Mining, Feb. 2018, pp.459\u2013467. https:\/\/doi.org\/10.1145\/3159652.3159706.","DOI":"10.1145\/3159652.3159706"},{"issue":"1","key":"1277_CR14","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1109\/MIC.2003.1167344","volume":"7","author":"G Linden","year":"2003","unstructured":"Linden G, Smith B, York J. Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Computing, 2003, 7(1): 76\u201380. https:\/\/doi.org\/10.1109\/MIC.2003.1167344.","journal-title":"IEEE Internet Computing"},{"key":"1277_CR15","doi-asserted-by":"publisher","unstructured":"Zhang H, Chen X, Ma S. Dynamic news recommendation with hierarchical attention network. In Proc. the 19th IEEE International Conference on Data Mining, Nov. 2019, pp.1456\u20131461. https:\/\/doi.org\/10.1109\/ICDM.2019.00190.","DOI":"10.1109\/ICDM.2019.00190"},{"key":"1277_CR16","doi-asserted-by":"publisher","unstructured":"Wang G, Guo Z Y, Li X, Yin D W, Ma S. SceneRec: Scene-based graph neural networks for recommender systems. In Proc. the 24th International Conference on Extending Database Technology, Mar. 2021, pp.397\u2013402. https:\/\/doi.org\/10.5441\/002\/EDBT.2021.41.","DOI":"10.5441\/002\/EDBT.2021.41"},{"key":"1277_CR17","doi-asserted-by":"publisher","unstructured":"Xie J R, Kelley S, Szymanski B K. Overlapping community detection in networks: The state-of-the-art and comparative study. ACM Computing Surveys, 2013, 45(4): Article No. 43. https:\/\/doi.org\/10.1145\/2501654.2501657.","DOI":"10.1145\/2501654.2501657"},{"issue":"3","key":"1277_CR18","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1080\/01969727308546046","volume":"3","author":"JC Dunn","year":"1973","unstructured":"Dunn J C. A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. Journal of Cybernetics, 1973, 3(3): 32\u201357. https:\/\/doi.org\/10.1080\/01969727308546046.","journal-title":"Journal of Cybernetics"},{"key":"1277_CR19","doi-asserted-by":"publisher","unstructured":"Lee D D, Seung H S. Algorithms for non-negative matrix factorization. In Proc. the 13th International Conference on Neural Information Processing Systems, Jan. 2000, pp.556\u2013562. https:\/\/doi.org\/10.5555\/3008751.3008829.","DOI":"10.5555\/3008751.3008829"},{"issue":"11","key":"1277_CR20","doi-asserted-by":"publisher","first-page":"2402","DOI":"10.1109\/TKDE.2017.2730207","volume":"29","author":"L Duan","year":"2017","unstructured":"Duan L, Ma S, Aggarwal C, Ma T J, Huai J P. An ensemble approach to link prediction. IEEE Trans. Knowledge and Data Engineering, 2017, 29(11): 2402\u20132416. https:\/\/doi.org\/10.1109\/TKDE.2017.2730207.","journal-title":"IEEE Trans. Knowledge and Data Engineering"},{"issue":"3","key":"1277_CR21","doi-asserted-by":"publisher","first-page":"493","DOI":"10.1007\/S10618-010-0181-Y","volume":"22","author":"F Wang","year":"2011","unstructured":"Wang F, Li T, Wang X, Zhu S H, Ding C. Community discovery using nonnegative matrix factorization. Data Mining and Knowledge Discovery, 2011, 22(3): 493\u2013521. https:\/\/doi.org\/10.1007\/S10618-010-0181-Y.","journal-title":"Data Mining and Knowledge Discovery"},{"key":"1277_CR22","doi-asserted-by":"publisher","unstructured":"Yang J, Leskovec J. Overlapping community detection at scale: A nonnegative matrix factorization approach. In Proc. the 6th ACM International Conference on Web Search and Data Mining, Feb. 2013, pp.587\u2013596. https:\/\/doi.org\/10.1145\/2433396.2433471.","DOI":"10.1145\/2433396.2433471"},{"key":"1277_CR23","doi-asserted-by":"publisher","unstructured":"Wang X, Jin D, Cao X C, Yang L, Zhang W X. Semantic community identification in large attribute networks. In Proc. the 30th AAAI Conference on Artificial Intelligence, Feb. 2016, pp.265\u2013271. https:\/\/doi.org\/10.5555\/3015812.3015851.","DOI":"10.5555\/3015812.3015851"},{"key":"1277_CR24","doi-asserted-by":"publisher","unstructured":"Kuang D, Park H, Ding C H Q. Symmetric nonnegative matrix factorization for graph clustering. In Proc. the 12th SIAM International Conference on Data Mining, Apr. 2012, pp.106\u2013117. https:\/\/doi.org\/10.1137\/1.9781611972825.10.","DOI":"10.1137\/1.9781611972825.10"},{"key":"1277_CR25","doi-asserted-by":"publisher","unstructured":"Yang J, McAuley J, Leskovec J. Community detection in networks with node attributes. In Proc. the 13th IEEE International Conference on Data Mining, Dec. 2013, pp.1151\u20131156. https:\/\/doi.org\/10.1109\/ICDM.2013.167.","DOI":"10.1109\/ICDM.2013.167"},{"key":"1277_CR26","doi-asserted-by":"publisher","unstructured":"Liu Y, Niculescu-Mizil A, Gryc W. Topic-link LDA: Joint models of topic and author community. In Proc. the 26th Annual International Conference on Machine Learning, Jun. 2009, pp.665\u2013672. https:\/\/doi.org\/10.1145\/1553374.1553460.","DOI":"10.1145\/1553374.1553460"},{"key":"1277_CR27","doi-asserted-by":"publisher","unstructured":"McAuley J, Leskovec J. Learning to discover social circles in ego networks. In Proc. the 25th International Conference on Neural Information Processing Systems, Dec. 2012, pp.539\u2013547. https:\/\/doi.org\/10.5555\/2999134.2999195.","DOI":"10.5555\/2999134.2999195"},{"key":"1277_CR28","doi-asserted-by":"publisher","unstructured":"Coscia M, Rossetti G, Giannotti F, Pedreschi D. DEMON: A local-first discovery method for overlapping communities. In Proc. the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Aug. 2012, pp.615\u2013623. https:\/\/doi.org\/10.1145\/2339530.2339630.","DOI":"10.1145\/2339530.2339630"},{"key":"1277_CR29","doi-asserted-by":"publisher","unstructured":"Zhang X C, Li H X, Liang W X, Luo J B. Multi-type coclustering of general heterogeneous information networks via nonnegative matrix tri-factorization. In Proc. the 16th IEEE International Conference on Data Mining, Dec. 2016, pp.1353\u20131358. https:\/\/doi.org\/10.1109\/ICDM.2016.0185.","DOI":"10.1109\/ICDM.2016.0185"},{"key":"1277_CR30","doi-asserted-by":"publisher","unstructured":"Zhou Y, Liu L, Buttler D. Integrating vertex-centric clustering with edge-centric clustering for meta path graph analysis. In Proc. the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Aug. 2015, pp.1563\u20131572. https:\/\/doi.org\/10.1145\/2783258.2783328.","DOI":"10.1145\/2783258.2783328"},{"key":"1277_CR31","doi-asserted-by":"publisher","unstructured":"Sun Y Z, Norick B, Han J W, Yan X F, Yu P S, Yu X. Pathselclus: Integrating meta-path selection with userguided object clustering in heterogeneous information networks. ACM Trans. Knowledge Discovery from Data, 2013, 7(3): Article No. 11. https:\/\/doi.org\/10.1145\/2500492.","DOI":"10.1145\/2500492"},{"key":"1277_CR32","doi-asserted-by":"publisher","unstructured":"Shi Y, He X W, Zhang N J, Yang C, Han J W. Userguided clustering in heterogeneous information networks via motif-based comprehensive transcription. In Proc. the 2019 European Conference on Machine Learning and Knowledge Discovery in Databases, Sept. 2019, pp.361\u2013377. https:\/\/doi.org\/10.1007\/978-3-030-46150-8_22.","DOI":"10.1007\/978-3-030-46150-8_22"},{"key":"1277_CR33","unstructured":"Elsner U. Graph partitioning: A survey. Technical Report, SFB 393\/97-27, Technische Universit\u00e4t Chemnitz, Chemnitz, Germany, 1997. https:\/\/core.ac.uk\/download\/pdf\/153227811.pdf, Jan. 2024."},{"key":"1277_CR34","doi-asserted-by":"publisher","unstructured":"Tumasjan A, Sprenger T O, Sandner P G, Welpe I M. Predicting elections with Twitter: What 140 characters reveal about political sentiment. In Proc. the 4th International Conference on Weblogs and Social Media, May 2010, pp.178\u2013152. https:\/\/doi.org\/10.1609\/icwsm.v4i1.14009.","DOI":"10.1609\/icwsm.v4i1.14009"},{"key":"1277_CR35","doi-asserted-by":"publisher","unstructured":"Azarbonyad H, Dehghani M, Beelen K, Arkut A, Marx M, Kamps J. Words are malleable: Computing semantic shifts in political and media discourse. In Proc. the 2017 ACM on Conference on Information and Knowledge Management, Nov. 2017, pp.1509\u20131518. https:\/\/doi.org\/10.1145\/3132847.3132878.","DOI":"10.1145\/3132847.3132878"},{"issue":"5","key":"1277_CR36","doi-asserted-by":"publisher","first-page":"833","DOI":"10.1109\/TKDE.2018.2849727","volume":"31","author":"P Cui","year":"2019","unstructured":"Cui P, Wang X, Pei J, Zhu W W. A survey on network embedding. IEEE Trans. Knowledge and Data Engineering, 2019, 31(5): 833\u2013852. https:\/\/doi.org\/10.1109\/TKDE.2018.2849727.","journal-title":"IEEE Trans. Knowledge and Data Engineering"},{"key":"1277_CR37","doi-asserted-by":"publisher","unstructured":"Lu Y F, Shi C, Hu L M, Liu Z Y. Relation structureaware heterogeneous information network embedding. In Proc. the 33rd AAAI Conference on Artificial Intelligence, Jul. 2019, pp.4456\u20134463. https:\/\/doi.org\/10.1609\/AAAI.V33I01.33014456.","DOI":"10.1609\/AAAI.V33I01.33014456"},{"key":"1277_CR38","doi-asserted-by":"publisher","unstructured":"Tang J, Qu M, Wang M Z, Zhang M, Yan J, Mei Q Z. LINE: Large-scale information network embedding. In Proc. the 24th International Conference on World Wide Web, May 2015, pp.1067\u20131077. https:\/\/doi.org\/10.1145\/2736277.2741093.","DOI":"10.1145\/2736277.2741093"},{"key":"1277_CR39","doi-asserted-by":"publisher","unstructured":"Wang D, Cui P, Zhu W. Structural deep network embedding. In Proc. the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Aug. 2016, pp.1225\u20131234. https:\/\/doi.org\/10.1145\/2939672.2939753.","DOI":"10.1145\/2939672.2939753"},{"key":"1277_CR40","doi-asserted-by":"publisher","unstructured":"Hu R J, Aggarwal C C, Ma S, Huai J. An embedding approach to anomaly detection. In Proc. the 32nd IEEE International Conference on Data Engineering, May 2016, pp.385\u2013396. https:\/\/doi.org\/10.1109\/ICDE.2016.7498256.","DOI":"10.1109\/ICDE.2016.7498256"},{"key":"1277_CR41","doi-asserted-by":"publisher","unstructured":"Perozzi B, Al-Rfou R, Skiena S. DeepWalk: Online learning of social representations. In Proc. the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Aug. 2014, pp.701\u2013710. https:\/\/doi.org\/10.1145\/2623330.2623732.","DOI":"10.1145\/2623330.2623732"},{"key":"1277_CR42","doi-asserted-by":"publisher","unstructured":"Grover A, Leskovec J. node2vec: Scalable feature learning for networks. In Proc. the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Aug. 2016, pp.855\u2013864. https:\/\/doi.org\/10.1145\/2939672.2939754.","DOI":"10.1145\/2939672.2939754"},{"key":"1277_CR43","doi-asserted-by":"publisher","unstructured":"Fu T Y, Lee W C, Lei Z. Hin2vec: Explore meta-paths in heterogeneous information networks for representation learning. In Proc. the 2017 ACM on Conference on Information and Knowledge Management, Nov. 2017, pp.1797\u20131806. https:\/\/doi.org\/10.1145\/3132847.3132953.","DOI":"10.1145\/3132847.3132953"},{"key":"1277_CR44","doi-asserted-by":"publisher","unstructured":"Dong Y X, Chawla N V, Swami A. metapath2vec: Scalable representation learning for heterogeneous networks. In Proc. the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Aug. 2017, pp.135\u2013144. https:\/\/doi.org\/10.1145\/3097983.3098036.","DOI":"10.1145\/3097983.3098036"},{"key":"1277_CR45","unstructured":"Kipf T N, Welling M. Semi-supervised classification with graph convolutional networks. In Proc. the 15th International Conference on Learning Representations, Apr. 2017."},{"key":"1277_CR46","doi-asserted-by":"publisher","unstructured":"Hamilton W L, Ying R, Leskovec J. Inductive representation learning on large graphs. In Proc. the 31st International Conference on Neural Information Processing Systems, Dec. 2017, pp.1024\u20131034. https:\/\/doi.org\/10.5555\/3294771.3294869.","DOI":"10.5555\/3294771.3294869"},{"key":"1277_CR47","doi-asserted-by":"publisher","unstructured":"Sun Y Z, Han J W. Mining Heterogeneous Information Networks: Principles and Methodologies. Springer, 2012: 1\u2013159. https:\/\/doi.org\/10.1007\/978-3-031-01902-9.","DOI":"10.1007\/978-3-031-01902-9"},{"issue":"2","key":"1277_CR48","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1145\/2481244.2481248","volume":"14","author":"YZ Sun","year":"2012","unstructured":"Sun Y Z, Han J W. Mining heterogeneous information networks: A structural analysis approach. ACM SIGKDD Explorations Newsletter, 2012, 14(2): 20\u201328. https:\/\/doi.org\/10.1145\/2481244.2481248.","journal-title":"ACM SIGKDD Explorations Newsletter"},{"key":"1277_CR49","doi-asserted-by":"publisher","unstructured":"Xu W, Liu X, Gong Y H. Document clustering based on non-negative matrix factorization. In Proc. the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Jul. 2003, pp.267\u2013273. https:\/\/doi.org\/10.1145\/860435.860485.","DOI":"10.1145\/860435.860485"},{"issue":"8","key":"1277_CR50","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1109\/MC.2009.263","volume":"42","author":"Y Koren","year":"2009","unstructured":"Koren Y, Bell R, Volinsky C. Matrix factorization techniques for recommender systems. Computer, 2009, 42(8): 30\u201337. https:\/\/doi.org\/10.1109\/MC.2009.263.","journal-title":"Computer"},{"key":"1277_CR51","doi-asserted-by":"publisher","unstructured":"Boyd S, Vandenberghe L. Convex Optimization. Cambridge University Press, 2004. https:\/\/doi.org\/10.1017\/CBO9780511804441.","DOI":"10.1017\/CBO9780511804441"},{"key":"1277_CR52","doi-asserted-by":"publisher","unstructured":"McAuley J, Targett C, Shi Q F, Van Den Hengel A. Image-based recommendations on styles and substitutes. In Proc. the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, Aug. 2015, pp.43\u201352. https:\/\/doi.org\/10.1145\/2766462.2767755.","DOI":"10.1145\/2766462.2767755"},{"key":"1277_CR53","doi-asserted-by":"publisher","unstructured":"Yang J, Leskovec J. Community-affiliation graph model for overlapping network community detection. In Proc. the 12th IEEE International Conference on Data Mining, Dec. 2012, pp.1170\u20131175. https:\/\/doi.org\/10.1109\/ICDM.2012.139.","DOI":"10.1109\/ICDM.2012.139"},{"issue":"3\/4\/5","key":"1277_CR54","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1016\/J.PHYSREP.2009.11.002","volume":"486","author":"S Fortunato","year":"2010","unstructured":"Fortunato S. Community detection in graphs. Physics Reports, 2010, 486(3\/4\/5): 75\u2013174. https:\/\/doi.org\/10.1016\/J.PHYSREP.2009.11.002.","journal-title":"Physics Reports"},{"key":"1277_CR55","unstructured":"Gregory S. Fuzzy overlapping communities in networks. arXiv: 1010.1523, 2010. https:\/\/arxiv.org\/abs\/1010.1523, Jan. 2024."}],"container-title":["Journal of Computer Science and Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11390-021-1277-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11390-021-1277-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11390-021-1277-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,11]],"date-time":"2024-04-11T07:10:54Z","timestamp":1712819454000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11390-021-1277-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,30]]},"references-count":55,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,2]]}},"alternative-id":["1277"],"URL":"https:\/\/doi.org\/10.1007\/s11390-021-1277-0","relation":{},"ISSN":["1000-9000","1860-4749"],"issn-type":[{"value":"1000-9000","type":"print"},{"value":"1860-4749","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,30]]},"assertion":[{"value":"8 January 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 October 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 January 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}