{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T14:01:20Z","timestamp":1760709680056,"version":"3.40.3"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319993645"},{"type":"electronic","value":"9783319993652"}],"license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018]]},"DOI":"10.1007\/978-3-319-99365-2_32","type":"book-chapter","created":{"date-parts":[[2018,8,11]],"date-time":"2018-08-11T09:15:03Z","timestamp":1533978903000},"page":"362-376","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Robust Detection of Communities with Multi-semantics in Large Attributed Networks"],"prefix":"10.1007","author":[{"given":"Di","family":"Jin","sequence":"first","affiliation":[]},{"given":"Ziyang","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Dongxiao","family":"He","sequence":"additional","affiliation":[]},{"given":"Bogdan","family":"Gabrys","sequence":"additional","affiliation":[]},{"given":"Katarzyna","family":"Musial","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2018,8,12]]},"reference":[{"key":"32_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.physrep.2016.09.002","volume":"659","author":"S Fortunato","year":"2016","unstructured":"Fortunato, S., Hric, D.: Community detection in networks: a user guide. Phys. Rep. 659, 1\u201344 (2016)","journal-title":"Phys. Rep."},{"issue":"12","key":"32_CR2","doi-asserted-by":"publisher","first-page":"7821","DOI":"10.1073\/pnas.122653799","volume":"99","author":"M Girvan","year":"2002","unstructured":"Girvan, M., Newman, M.: Community structure in social and biological networks. Proc. Nat. Acad. Sci. 99(12), 7821\u20137826 (2002)","journal-title":"Proc. Nat. Acad. Sci."},{"issue":"1","key":"32_CR3","doi-asserted-by":"publisher","first-page":"013044","DOI":"10.1088\/1367-2630\/17\/1\/013044","volume":"17","author":"S Jia","year":"2015","unstructured":"Jia, S., Gao, L., Gao, Y., et al.: Defining and identifying cograph communities in complex networks. New J. Phys. 17(1), 013044 (2015)","journal-title":"New J. Phys."},{"key":"32_CR4","unstructured":"Yang, L., Cao, X., He, D., et al.: Modularity based community detection with deep learning. In: International Joint Conference on Artificial Intelligence (IJCAI), New York, USA, pp. 2252\u20132258 (2016)"},{"issue":"2","key":"32_CR5","doi-asserted-by":"publisher","first-page":"022302","DOI":"10.1103\/PhysRevE.95.022302","volume":"95","author":"M Fanuel","year":"2017","unstructured":"Fanuel, M., Alaiz, C.M., Suykens, J.A., et al.: Magnetic eigenmaps for community detection in directed networks. Phys. Rev. E 95(2), 022302 (2017)","journal-title":"Phys. Rev. E"},{"issue":"1","key":"32_CR6","doi-asserted-by":"publisher","first-page":"250","DOI":"10.1109\/JSYST.2015.2433294","volume":"11","author":"F Hao","year":"2017","unstructured":"Hao, F., Min, G., Pei, Z., et al.: K-clique community detection in social networks based on formal concept analysis. IEEE Syst. J. 11(1), 250\u2013259 (2017)","journal-title":"IEEE Syst. J."},{"issue":"5","key":"32_CR7","doi-asserted-by":"publisher","first-page":"1272","DOI":"10.1109\/TKDE.2016.2518687","volume":"28","author":"JJ Whang","year":"2016","unstructured":"Whang, J.J., Gleich, D.F., Dhillon, I.S., et al.: Overlapping community detection using neighborhood-inflated seed expansion. IEEE Trans. Knowl. Data Eng. 28(5), 1272\u20131284 (2016)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"32_CR8","doi-asserted-by":"crossref","unstructured":"Jin, D., Wang, H., Dang, J., et al.: Detect overlapping communities via modeling and ranking node popularities. In: 30th AAAI Conference on Artificial Intelligence, Phoenix, Arizona, USA, pp. 172\u2013178 (2016)","DOI":"10.1609\/aaai.v30i1.9981"},{"issue":"147","key":"32_CR9","first-page":"1","volume":"17","author":"T Van Laarhoven","year":"2016","unstructured":"Van Laarhoven, T., Marchiori, E.: Local network community detection with continuous optimization of conductance and weighted kernel k-means. J. Mach. Learn. Res. 17(147), 1\u201328 (2016)","journal-title":"J. Mach. Learn. Res."},{"key":"32_CR10","doi-asserted-by":"crossref","unstructured":"Jin, D., Wang, X., He, R., et al.: Robust detection of link communities in large social networks by exploiting link semantics. In: 32th AAAI Conference on Artificial Intelligence, New Orleans, Louisiana, USA (2018)","DOI":"10.1609\/aaai.v32i1.11283"},{"key":"32_CR11","doi-asserted-by":"crossref","unstructured":"He, D., Feng, Z., Jin, D., et al.: Joint identification of network communities and semantics via integrative modeling of network topologies and node contents. In: 31th AAAI Conference on Artificial Intelligence, San Francisco, California, USA (2017)","DOI":"10.1609\/aaai.v31i1.10489"},{"key":"32_CR12","doi-asserted-by":"crossref","unstructured":"Akbari, M., Chua, T.S.: Leveraging behavioral factorization and prior knowledge for community discovery and profiling. In: Web Search and Data Mining (WSDM), UK, pp. 71\u201379 (2017)","DOI":"10.1145\/3018661.3018693"},{"issue":"7","key":"32_CR13","doi-asserted-by":"publisher","first-page":"817","DOI":"10.14778\/3067421.3067430","volume":"10","author":"H Cai","year":"2017","unstructured":"Cai, H., Zheng, V.W., Zhu, F., et al.: From community detection to community profiling. Proc. VLDB Endow. 10(7), 817\u2013828 (2017)","journal-title":"Proc. VLDB Endow."},{"key":"32_CR14","doi-asserted-by":"crossref","unstructured":"Wang, X., Jin, D., Cao, X., et al.: Semantic community identification in large attribute networks. In: 30th AAAI Conference on Artificial Intelligence, Phoenix, Arizona, USA, pp. 265\u2013271 (2016)","DOI":"10.1609\/aaai.v30i1.9977"},{"issue":"6","key":"32_CR15","doi-asserted-by":"publisher","first-page":"927","DOI":"10.1016\/S0893-6080(05)80089-9","volume":"5","author":"E Oja","year":"1992","unstructured":"Oja, E.: Principal components, minor components, and linear neural networks. Neural Netw. 5(6), 927\u2013935 (1992)","journal-title":"Neural Netw."},{"issue":"1","key":"32_CR16","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1198\/0003130042836","volume":"58","author":"DR Hunter","year":"2004","unstructured":"Hunter, D.R., Lange, K.A.: A tutorial on mm algorithms. Am. Stat. 58(1), 30\u201337 (2004)","journal-title":"Am. Stat."},{"issue":"3","key":"32_CR17","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1609\/aimag.v29i3.2157","volume":"29","author":"P Sen","year":"2008","unstructured":"Sen, P., Namata, G., Bilgic, M., et al.: Collective classification in network data. AI Mag. 29(3), 93\u2013106 (2008)","journal-title":"AI Mag."},{"key":"32_CR18","unstructured":"Leskovec, J.: Stanford Network Analysis Project (2016). http:\/\/snap.stanford.edu"},{"issue":"7","key":"32_CR19","first-page":"1299","volume":"34","author":"H Liu","year":"2012","unstructured":"Liu, H., Wu, Z., Li, X., et al.: Constrained nonnegative matrix factorization for image representation. IEEE Trans. Softw. Eng. 34(7), 1299\u20131311 (2012)","journal-title":"IEEE Trans. Softw. Eng."},{"key":"32_CR20","doi-asserted-by":"crossref","unstructured":"Yang, J., Mcauley, J., Leskovec, J., et al.: Community detection in networks with node attributes. In: the IEEE International Conference on Data Mining series (ICDM), Dallas, Texas, USA, pp. 1151\u20131156 (2013)","DOI":"10.1109\/ICDM.2013.167"},{"issue":"1","key":"32_CR21","doi-asserted-by":"publisher","first-page":"016107","DOI":"10.1103\/PhysRevE.83.016107","volume":"83","author":"B Karrer","year":"2011","unstructured":"Karrer, B., Newman, M.: Stochastic blockmodels and community structure in networks. Phys. Rev. E 83(1), 016107 (2011)","journal-title":"Phys. Rev. E"},{"key":"32_CR22","doi-asserted-by":"crossref","unstructured":"Yang, J., Leskovec, J.: Overlapping community detection at scale: a nonnegative matrix factorization approach. In: Web Search and Data Mining (WSDM), Rome, Italy, pp. 587\u2013596 (2013)","DOI":"10.1145\/2433396.2433471"},{"issue":"5814","key":"32_CR23","doi-asserted-by":"publisher","first-page":"972","DOI":"10.1126\/science.1136800","volume":"315","author":"BJ Frey","year":"2007","unstructured":"Frey, B.J., Dueck, D.: Clustering by passing messages between data points. Science 315(5814), 972\u2013976 (2007)","journal-title":"Science"},{"key":"32_CR24","doi-asserted-by":"crossref","unstructured":"Yang, J., Mcauley, J., Leskovec, J., et al.: Community detection in networks with node attributes. In: the IEEE International Conference on Data Mining series (ICDM), Dallas, Texas, USA, pp. 1151\u20131156 (2013)","DOI":"10.1109\/ICDM.2013.167"},{"issue":"2","key":"32_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2517088","volume":"5","author":"S Pool","year":"2014","unstructured":"Pool, S., Bonchi, F., Van Leeuwen, M., et al.: Description-driven community detection. ACM Trans. Intell. Syst. Technol. 5(2), 1\u201328 (2014)","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"32_CR26","doi-asserted-by":"crossref","unstructured":"Yang, T., Jin, R., Chi, Y., et al.: Combining link and content for community detection: a discriminative approach. In: 13th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), Paris, France, pp. 927\u2013936 (2009)","DOI":"10.1145\/1557019.1557120"},{"key":"32_CR27","doi-asserted-by":"crossref","unstructured":"Balasubramanyan, R., Cohen, W.W.: Block-LDA: jointly modeling entity-annotated text and entity-entity links. In: SIAM International Conference on Data Mining (SDM), Mesa, Arizona, USA, pp. 450\u2013461 (2011)","DOI":"10.1137\/1.9781611972818.39"},{"key":"32_CR28","doi-asserted-by":"crossref","unstructured":"Kido, G.S., Igawa, R.A., Barbon Jr, S.: Topic modeling based on louvain method in online social networks. In: Proceedings of XII Brazilian Symposium on Information Systems, Florian\u00f3polis, SC, pp. 353\u2013360 (2016)","DOI":"10.5753\/sbsi.2016.5982"}],"container-title":["Lecture Notes in Computer Science","Knowledge Science, Engineering and Management"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-99365-2_32","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T17:38:54Z","timestamp":1709833134000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-319-99365-2_32"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"ISBN":["9783319993645","9783319993652"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-99365-2_32","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2018]]},"assertion":[{"value":"12 August 2018","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"KSEM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Knowledge Science, Engineering and Management","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Changchun","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 August 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 August 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ksem2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/ksem2018.venue.link\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"262","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"62","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"26","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"24% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.1","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"10","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"We have 3 reviews for 235 submissions, 4 reviews for 25 submissions and 5 review for 2 submissions.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}