{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T20:42:39Z","timestamp":1742935359599,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":23,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819972531"},{"type":"electronic","value":"9789819972548"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-981-99-7254-8_34","type":"book-chapter","created":{"date-parts":[[2023,10,21]],"date-time":"2023-10-21T05:01:47Z","timestamp":1697864507000},"page":"439-450","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Link Prediction for\u00a0Opportunistic Networks Based on\u00a0Hybrid Similarity Metrics and\u00a0E-LSTM-D Models"],"prefix":"10.1007","author":[{"given":"Xiaoying","family":"Yang","sequence":"first","affiliation":[]},{"given":"Lijie","family":"Li","sequence":"additional","affiliation":[]},{"given":"Gang","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Ming","family":"Song","sequence":"additional","affiliation":[]},{"given":"Feng","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,21]]},"reference":[{"issue":"11","key":"34_CR1","doi-asserted-by":"publisher","DOI":"10.1002\/dac.4860","volume":"34","author":"R Sachdeva","year":"2021","unstructured":"Sachdeva, R., Dev, A.: Review of opportunistic network: assessing past, present, and future. Int. J. Commun Syst 34(11), e4860 (2021)","journal-title":"Int. J. Commun Syst"},{"key":"34_CR2","doi-asserted-by":"crossref","unstructured":"Fall, K.: A delay-tolerant network architecture for challenged internets. In: Proceedings of the 2003 Conference on Applications, Technologies, Architectures, and Protocols for Computer Communications, pp. 27\u201334 (2003)","DOI":"10.1145\/863955.863960"},{"issue":"03","key":"34_CR3","first-page":"214","volume":"30","author":"Z Yao","year":"2021","unstructured":"Yao, Z.: Trusted routing model based on opportunity network in vehicular networking environment. Comput. Syst. Appl. 30(03), 214\u2013220 (2021)","journal-title":"Comput. Syst. Appl."},{"key":"34_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.adhoc.2021.102560","volume":"120","author":"X Pang","year":"2021","unstructured":"Pang, X., Liu, M., Li, Z.: Geographic position based hopless opportunistic routing for UAV networks. Ad Hoc Netw. 120, 102560 (2021)","journal-title":"Ad Hoc Netw."},{"key":"34_CR5","unstructured":"Juan, H.: Theoretical research related to the information triage mechanism of landslide disaster based on opportunity network. Guizhou University (2022)"},{"key":"34_CR6","first-page":"7601316","volume":"2016","author":"R Mart\u00ednez-Vidal","year":"2016","unstructured":"Mart\u00ednez-Vidal, R., Mart\u00ed, R., Sreenan, J.C.: Measuring QoS in an aeronautical opportunistic network architecture with limited access to a satellite communications backhaul. Mob. Inf. Syst. 2016, 7601316 (2016)","journal-title":"Mob. Inf. Syst."},{"key":"34_CR7","unstructured":"Wenhui, M.: Research on the application of wildlife location tracking technology based on wireless sensor network. Guizhou University (2018)"},{"key":"34_CR8","doi-asserted-by":"publisher","first-page":"35874","DOI":"10.1109\/ACCESS.2019.2905420","volume":"7","author":"H Zhang","year":"2019","unstructured":"Zhang, H., Chen, Z., Wu, J.: FRRF: a fuzzy reasoning routing-forwarding algorithm using mobile device similarity in mobile edge computing-based opportunistic mobile social networks. IEEE Access 7, 35874\u201335889 (2019)","journal-title":"IEEE Access"},{"issue":"1","key":"34_CR9","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1080\/0022250X.1971.9989788","volume":"1","author":"F Lorrain","year":"1979","unstructured":"Lorrain, F., White, H.C.: Structural equivalence of individuals in social networks. J. Math. Sociol. 1(1), 49\u201380 (1979)","journal-title":"J. Math. Sociol."},{"issue":"08","key":"34_CR10","first-page":"1798","volume":"37","author":"C Sha","year":"2016","unstructured":"Sha, C., Fuxi, Z.: A dynamic link prediction method for networks based on hybrid similarity metrics. Small Microcomput. Syst. 37(08), 1798\u20131801 (2016)","journal-title":"Small Microcomput. Syst."},{"issue":"05","key":"34_CR11","first-page":"265","volume":"47","author":"Y Rong","year":"2020","unstructured":"Rong, Y., Yurong, S., Fanrong, M.: A link prediction method based on weighted network topology weights. Comput. Sci. 47(05), 265\u2013270 (2020)","journal-title":"Comput. Sci."},{"issue":"01","key":"34_CR12","first-page":"98","volume":"62","author":"Y Yong","year":"2022","unstructured":"Yong, Y.: Link prediction algorithm based on clustering coefficients and node centrality. J. Tsinghua Univ. 62(01), 98\u2013104 (2022)","journal-title":"J. Tsinghua Univ."},{"key":"34_CR13","doi-asserted-by":"crossref","unstructured":"Ahmed, A.: Distributed large-scale natural graph factorization. In: Proceedings of the 22nd International Conference on World Wide Web, pp. 37\u201348 (2013)","DOI":"10.1145\/2488388.2488393"},{"key":"34_CR14","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1016\/j.physa.2017.12.092","volume":"496","author":"X Ma","year":"2018","unstructured":"Ma, X., Sun, P., Wang, Y.: Graph regularized nonnegative matrix factorization for temporal link prediction in dynamic networks. Phys. A 496, 121\u2013136 (2018)","journal-title":"Phys. A"},{"key":"34_CR15","doi-asserted-by":"crossref","unstructured":"Perozzi, B., Al-Rfou, R., Skiena, S.: DeepWalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 701\u2013710 (2014)","DOI":"10.1145\/2623330.2623732"},{"key":"34_CR16","doi-asserted-by":"crossref","unstructured":"Tang, J., Qu, M., Wang, M.: LINE: large-scale information network embedding. In: Proceedings of the 24th International Conference on World Wide Web, pp. 1067\u20131077 (2015)","DOI":"10.1145\/2736277.2741093"},{"key":"34_CR17","doi-asserted-by":"crossref","unstructured":"Grover, A., Leskovec, J.: node2vec: scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855\u2013864 (2016)","DOI":"10.1145\/2939672.2939754"},{"issue":"6","key":"34_CR18","doi-asserted-by":"publisher","first-page":"3699","DOI":"10.1109\/TSMC.2019.2932913","volume":"51","author":"J Chen","year":"2019","unstructured":"Chen, J., Zhang, J., Xu, X.: E-LSTM-D: a deep learning framework for dynamic network link prediction. IEEE Trans. Syst. Man Cybern. Syst. 51(6), 3699\u20133712 (2019)","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"key":"34_CR19","doi-asserted-by":"crossref","unstructured":"Lei, K., Qin, M., Bai, B.: GCN-GAN: a non-linear temporal link prediction model for weighted dynamic networks. In: IEEE INFOCOM 2019-IEEE Conference on Computer Communications, pp. 388\u2013396. IEEE (2019)","DOI":"10.1109\/INFOCOM.2019.8737631"},{"key":"34_CR20","doi-asserted-by":"crossref","unstructured":"Shu, J.: Link prediction based on 3D convolutional neural network. In: 2022 IEEE\/CIC International Conference on Communications in China, pp. 156\u2013161 (2022)","DOI":"10.1109\/ICCC55456.2022.9880708"},{"key":"34_CR21","unstructured":"Linlan, L., Xiuyang, S., Yubin, C.: Opportunity network link prediction based on network representation learning. J. Beijing Univ. Posts Telecommun. 45(04), 64\u201369+103 (2022)"},{"key":"34_CR22","doi-asserted-by":"publisher","first-page":"29219","DOI":"10.1109\/ACCESS.2018.2839770","volume":"6","author":"T Li","year":"2018","unstructured":"Li, T., Zhang, J., Philip, S.Y.: Deep dynamic network embedding for link prediction. IEEE Access 6, 29219\u201329230 (2018)","journal-title":"IEEE Access"},{"key":"34_CR23","doi-asserted-by":"crossref","unstructured":"Sankar, A., Wu, Y., Gou, L.: DySAT: deep neural representation learning on dynamic graphs via self-attention networks. In: Proceedings of the 13th International Conference on Web Search and Data Mining, pp. 519\u2013527 (2020)","DOI":"10.1145\/3336191.3371845"}],"container-title":["Lecture Notes in Computer Science","Web Information Systems Engineering \u2013 WISE 2023"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-7254-8_34","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,21]],"date-time":"2023-10-21T05:07:57Z","timestamp":1697864877000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-7254-8_34"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9789819972531","9789819972548"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-7254-8_34","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"21 October 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"WISE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Web Information Systems Engineering","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Melbourne, VIC","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Australia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"wise2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.wise-conferences.org\/2023\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-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":"137","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":"33","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":"40","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","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":"4","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)"}}]}}