{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T06:58:32Z","timestamp":1760597912130,"version":"3.38.0"},"reference-count":48,"publisher":"SAGE Publications","issue":"5","license":[{"start":{"date-parts":[[2019,12,8]],"date-time":"2019-12-08T00:00:00Z","timestamp":1575763200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Information Science"],"published-print":{"date-parts":[[2021,10]]},"abstract":"<jats:p> Recently, link prediction has attracted more attention from various disciplines such as computer science, bioinformatics and economics. In link prediction, numerous information such as network topology, profile information and user-generated contents are considered to discover missing links between nodes. Whereas numerous previous researches had focused on the structural features of the networks for link prediction, recent studies have shown more interest in profile and content information, too. So, some of these researches combine structural and content information. However, some issues such as scalability and feature engineering need to be investigated to solve a few remaining problems. Moreover, most of the previous researches are presented only for undirected and unweighted networks. In this article, a novel link prediction framework named \u2018DeepLink\u2019 is presented, which is based on deep learning techniques. While deep learning has the advantage of extracting automatically the best features for link prediction, many other link prediction algorithms need manual feature engineering. Moreover, in the proposed framework, both structural and content information are employed. The framework is capable of using different structural feature vectors that are prepared by various link prediction methods. It learns all proximity orders that are presented on a network during the structural feature learning. We have evaluated the effectiveness of DeepLink on two real social network datasets, Telegram and irBlogs. On both datasets, the proposed framework outperforms several other structural and hybrid approaches for link prediction. <\/jats:p>","DOI":"10.1177\/0165551519891345","type":"journal-article","created":{"date-parts":[[2019,12,8]],"date-time":"2019-12-08T19:50:15Z","timestamp":1575834615000},"page":"642-657","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":15,"title":["DeepLink: A novel link prediction framework based on deep learning"],"prefix":"10.1177","volume":"47","author":[{"given":"Mohammad Mehdi","family":"Keikha","sequence":"first","affiliation":[{"name":"University of Sistan and Baluchestan, Iran; Database Research Group, Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0836-2642","authenticated-orcid":false,"given":"Maseud","family":"Rahgozar","sequence":"additional","affiliation":[{"name":"Database Research Group, Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Iran"}]},{"given":"Masoud","family":"Asadpour","sequence":"additional","affiliation":[{"name":"Database Research Group, Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Iran"}]}],"member":"179","published-online":{"date-parts":[[2019,12,8]]},"reference":[{"key":"bibr1-0165551519891345","doi-asserted-by":"publisher","DOI":"10.1002\/asi.20591"},{"key":"bibr2-0165551519891345","doi-asserted-by":"publisher","DOI":"10.1007\/s13721-012-0005-7"},{"key":"bibr3-0165551519891345","doi-asserted-by":"publisher","DOI":"10.1186\/s12918-017-0463-8"},{"key":"bibr4-0165551519891345","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2012.01.202"},{"key":"bibr5-0165551519891345","doi-asserted-by":"publisher","DOI":"10.1145\/2180861.2180866"},{"first-page":"1285","volume-title":"Proceedings of the 18th ACM SIGKDD international conference on knowledge discovery and data mining (KDD \u201912)","author":"Tang J","key":"bibr6-0165551519891345"},{"first-page":"292","volume-title":"Proceedings of the 2011 IEEE international conference on information reuse and integration (IRI 2011)","author":"Akcora CG","key":"bibr7-0165551519891345"},{"key":"bibr8-0165551519891345","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2018.05.027"},{"key":"bibr9-0165551519891345","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2018.04.034"},{"key":"bibr10-0165551519891345","doi-asserted-by":"publisher","DOI":"10.1016\/S0378-8733(03)00009-1"},{"key":"bibr11-0165551519891345","doi-asserted-by":"publisher","DOI":"10.1007\/BF02289026"},{"key":"bibr12-0165551519891345","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.64.025102"},{"first-page":"138","volume-title":"Proceedings of the 27th annual ACM symposium on applied computing","author":"Chen H-H","key":"bibr13-0165551519891345"},{"first-page":"1019","volume-title":"Proceedings of the 21st international conference on World Wide Web","author":"Lichtenwalter RN","key":"bibr14-0165551519891345"},{"first-page":"1147","volume-title":"Proceedings of the 20th ACM international conference on information and knowledge management","author":"Li R-H","key":"bibr15-0165551519891345"},{"key":"bibr16-0165551519891345","doi-asserted-by":"publisher","DOI":"10.1007\/s13278-013-0142-8"},{"first-page":"703","volume-title":"Proceedings of the fifth ACM international conference on Web search and data mining (WSDM \u201912)","author":"Anderson A","key":"bibr17-0165551519891345"},{"key":"bibr18-0165551519891345","doi-asserted-by":"publisher","DOI":"10.1007\/s13278-010-0006-4"},{"key":"bibr19-0165551519891345","doi-asserted-by":"publisher","DOI":"10.1007\/s13278-012-0090-8"},{"key":"bibr20-0165551519891345","doi-asserted-by":"crossref","unstructured":"Mohamed A, Yu D, Deng L. Investigation of full-sequence training of deep belief networks for speech recognition. In: Interspeech 2010, Chiba, Japan, 26\u201330 September 2010, pp. 2846\u20132849, https:\/\/www.isca-speech.org\/archive\/archive_papers\/interspeech_2010\/i10_2846.pdf","DOI":"10.21437\/Interspeech.2010-304"},{"key":"bibr21-0165551519891345","unstructured":"Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. In: Proceedings of the 25th international conference on neural information processing systems, Lake Tahoe, NV, 3\u20136 December 2012, pp. 1097\u20131105, https:\/\/papers.nips.cc\/paper\/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf"},{"key":"bibr22-0165551519891345","doi-asserted-by":"publisher","DOI":"10.1111\/j.1756-8765.2010.01109.x"},{"first-page":"701","volume-title":"Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD \u201914)","author":"Perozzi B","key":"bibr23-0165551519891345"},{"key":"bibr24-0165551519891345","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2018.02.028"},{"first-page":"431","volume-title":"Proceedings of the 2016 ACM on multimedia conference (MM \u201916)","author":"Tang M","key":"bibr25-0165551519891345"},{"first-page":"855","volume-title":"Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining","author":"Grover A","key":"bibr26-0165551519891345"},{"key":"bibr27-0165551519891345","unstructured":"Mikolov T, Chen K, Corrado G, et al. Distributed representations of words and phrases and their compositionality. In: NIPS\u201913 Proceedings of the 26th international conference on neural information processing systems, Lake Tahoe, NV, 5\u201310 December 2013, pp. 3111\u20133119, https:\/\/papers.nips.cc\/paper\/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf"},{"key":"bibr28-0165551519891345","unstructured":"Le Q, Mikolov T. Distributed representations of sentences and documents. In: Proceedings of the 31st international conference on international conference on machine learning, vol. 32, Beijing, China, 21\u201326 June 2014, pp. II-1188\u2013II-1196, http:\/\/proceedings.mlr.press\/v32\/le14.pdf"},{"key":"bibr29-0165551519891345","first-page":"1","volume":"58","author":"Wang P","year":"2014","journal-title":"Sci China Inf Sci"},{"key":"bibr30-0165551519891345","doi-asserted-by":"publisher","DOI":"10.1016\/S0378-4371(02)00736-7"},{"key":"bibr31-0165551519891345","doi-asserted-by":"publisher","DOI":"10.1140\/epjb\/e2009-00335-8"},{"key":"bibr32-0165551519891345","doi-asserted-by":"publisher","DOI":"10.1016\/j.jocs.2018.04.006"},{"key":"bibr33-0165551519891345","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.80.046122"},{"key":"bibr34-0165551519891345","doi-asserted-by":"publisher","DOI":"10.1177\/0165551514560121"},{"first-page":"538","volume-title":"Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining (KDD \u201902)","author":"Jeh G","key":"bibr35-0165551519891345"},{"key":"bibr36-0165551519891345","doi-asserted-by":"publisher","DOI":"10.1177\/0165551516664039"},{"key":"bibr37-0165551519891345","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.76.036106"},{"first-page":"1046","volume-title":"Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD \u201911)","author":"Scellato S","key":"bibr38-0165551519891345"},{"key":"bibr39-0165551519891345","doi-asserted-by":"publisher","DOI":"10.1016\/j.dss.2012.09.019"},{"first-page":"1067","volume-title":"Proceedings of the 24th international conference on World Wide Web","author":"Tang J","key":"bibr40-0165551519891345"},{"key":"bibr41-0165551519891345","unstructured":"Wang X, Cui P, Wang J, et al. Community preserving network embedding. In: Proceedings of the thirty-first AAAI conference on artificial intelligence (AAAI-17), San Francisco, CA, 4\u20139 February 2017, pp. 203\u2013209, https:\/\/aaai.org\/ocs\/index.php\/AAAI\/AAAI17\/paper\/view\/14589\/13763"},{"key":"bibr42-0165551519891345","doi-asserted-by":"publisher","DOI":"10.1088\/1742-5468\/2008\/10\/P10008"},{"key":"bibr43-0165551519891345","unstructured":"Mikolov T, Chen K, Corrado G, et al. Efficient estimation of word representations in vector space, http:\/\/arxiv.org\/abs\/1301.3781 (2013, accessed 3 July 2018)."},{"key":"bibr44-0165551519891345","unstructured":"Khan BS, Niazi MA. Network community detection: a review and visual survey, https:\/\/arxiv.org\/ftp\/arxiv\/papers\/1708\/1708.00977.pdf (accessed 29 November 2018)."},{"key":"bibr45-0165551519891345","doi-asserted-by":"publisher","DOI":"10.1016\/j.chb.2015.11.038"},{"key":"bibr46-0165551519891345","unstructured":"Lobao M. Telegram v3.2 brings channels for broadcasting your messages to the World, https:\/\/www.androidpolice.com\/2015\/09\/22\/telegram-v3-2-brings-channels-broadcasting-messages-world\/ (accessed 11 March 2018)."},{"key":"bibr47-0165551519891345","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2010.11.027"},{"key":"bibr48-0165551519891345","doi-asserted-by":"publisher","DOI":"10.1145\/3012704"}],"container-title":["Journal of Information Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/0165551519891345","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/0165551519891345","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/0165551519891345","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T08:45:26Z","timestamp":1740818726000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/0165551519891345"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12,8]]},"references-count":48,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2021,10]]}},"alternative-id":["10.1177\/0165551519891345"],"URL":"https:\/\/doi.org\/10.1177\/0165551519891345","relation":{},"ISSN":["0165-5515","1741-6485"],"issn-type":[{"type":"print","value":"0165-5515"},{"type":"electronic","value":"1741-6485"}],"subject":[],"published":{"date-parts":[[2019,12,8]]}}}