{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,18]],"date-time":"2026-02-18T00:03:11Z","timestamp":1771372991841,"version":"3.50.1"},"publisher-location":"Cham","reference-count":41,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031509582","type":"print"},{"value":"9783031509599","type":"electronic"}],"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\/978-3-031-50959-9_40","type":"book-chapter","created":{"date-parts":[[2023,12,30]],"date-time":"2023-12-30T10:02:35Z","timestamp":1703930555000},"page":"580-595","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Link Prediction for\u00a0Attribute and\u00a0Structure Learning Based on\u00a0Attention Mechanism"],"prefix":"10.1007","author":[{"given":"Renjuan","family":"Nie","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guoyin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qun","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chengxin","family":"Peng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,12,31]]},"reference":[{"issue":"3","key":"40_CR1","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1016\/S0378-8733(03)00009-1","volume":"25","author":"LA Adamic","year":"2003","unstructured":"Adamic, L.A., Adar, E.: Friends and neighbors on the web. Soc. Netw. 25(3), 211\u2013230 (2003)","journal-title":"Soc. Netw."},{"key":"40_CR2","unstructured":"Airoldi, E.M., Blei, D.M., Fienberg, S.E., Xing, E.P.: Mixed membership stochastic blockmodels (2008)"},{"key":"40_CR3","unstructured":"Avdeeva, E., Herczeg, T., Stalerunaite, B., Andreasen, V.: Epidemic spread in scale-free networks (2009)"},{"key":"40_CR4","unstructured":"Benedek, R., Carl, A., Rik, S.: Multi-scale attributed node embedding. J. Complex Netw. (2), 2 (2021)"},{"key":"40_CR5","unstructured":"Bojchevski, A., G\u00fcnnemann, S.: Deep gaussian embedding of graphs: unsupervised inductive learning via ranking (2017)"},{"key":"40_CR6","unstructured":"Chami, I., Ying, R., R\u00e9, C., Leskovec, J.: Hyperbolic graph convolutional neural networks. CoRR abs\/1910.12933 (2019). http:\/\/arxiv.org\/1910.12933"},{"key":"40_CR7","unstructured":"Chami, I., Ying, R., R\u00e9, C., Leskovec, J.: Hyperbolic graph convolutional neural networks (2019)"},{"key":"40_CR8","first-page":"4869","volume":"32","author":"I Chami","year":"2019","unstructured":"Chami, I., Ying, R., R\u00e9, C., Leskovec, J.: Hyperbolic graph convolutional neural networks. Adv. Neural. Inf. Process. Syst. 32, 4869\u20134880 (2019)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"40_CR9","doi-asserted-by":"crossref","unstructured":"Fu, X., Zhang, J., Meng, Z., King, I.: MAGNN: metapath aggregated graph neural network for heterogeneous graph embedding (2020)","DOI":"10.1145\/3366423.3380297"},{"key":"40_CR10","doi-asserted-by":"crossref","unstructured":"Grover, A., Leskovec, J.: Node2vec: scalable feature learning for networks (2016)","DOI":"10.1145\/2939672.2939754"},{"key":"40_CR11","unstructured":"Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs (2017)"},{"key":"40_CR12","doi-asserted-by":"crossref","unstructured":"Huang, Z., Zhang, S., Xi, C., Liu, T., Zhou, M.: Scaling up graph neural networks via graph coarsening (2021)","DOI":"10.1145\/3447548.3467256"},{"key":"40_CR13","doi-asserted-by":"crossref","unstructured":"Jin, Y., Song, G., Shi, C.: GraLSP: graph neural networks with local structural patterns. pp. 4361\u20134368 (2020)","DOI":"10.1609\/aaai.v34i04.5861"},{"key":"40_CR14","unstructured":"Kim, D., Oh, A.: How to find your friendly neighborhood: graph attention design with self-supervision. arXiv e-prints (2022)"},{"key":"40_CR15","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks (2016)"},{"key":"40_CR16","unstructured":"Kipf, T.N., Welling, M.: Variational graph auto-encoders (2016)"},{"issue":"8","key":"40_CR17","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 42(8), 30\u201337 (2009)","journal-title":"Computer"},{"key":"40_CR18","unstructured":"Li, B., Zhou, M., Zhang, S., Yang, M., Lian, D., Huang, Z.: BSAL: a framework of bi-component structure and attribute learning for link prediction (2022)"},{"issue":"3","key":"40_CR19","first-page":"6","volume":"50","author":"ZT Li Yanli","year":"2021","unstructured":"Li Yanli, Z.T.: Local similarity indices in link prediction (in Chinese). J. Univ. Electron. Sci. Technol. 50(3), 6 (2021)","journal-title":"J. Univ. Electron. Sci. Technol."},{"key":"40_CR20","doi-asserted-by":"crossref","unstructured":"Liben-Nowell, D.: The link prediction problem for social networks. In: Conference on Information and Knowledge Management. Conference on Information and Knowledge Management (2003)","DOI":"10.1145\/956863.956972"},{"issue":"5","key":"40_CR21","first-page":"11","volume":"39","author":"L Linyuan","year":"2010","unstructured":"Linyuan, L.: Link prediction on complex networks (in Chinese). J. Univ. Electron. Sci. Technol. China 39(5), 11 (2010)","journal-title":"J. Univ. Electron. Sci. Technol. China"},{"key":"40_CR22","unstructured":"Liu, Q., Tan, H., Zhang, Y., Wang, G.: Dynamic heterogeneous network representation method based on meta-path (in chinese). Acta Electron. Sinica (008), 050 (2022)"},{"key":"40_CR23","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1016\/j.neucom.2021.12.079","volume":"476","author":"H Lu","year":"2022","unstructured":"Lu, H., Hu, H., Lin, X.: DensE: an enhanced non-commutative representation for knowledge graph embedding with adaptive semantic hierarchy. Neurocomputing 476, 115\u2013125 (2022)","journal-title":"Neurocomputing"},{"issue":"1","key":"40_CR24","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1109\/JPROC.2015.2483592","volume":"104","author":"M Nickel","year":"2016","unstructured":"Nickel, M., Tresp, V., Murphy, K., Gabrilovich, E.: A review of relational machine learning for knowledge graphs. Proc. IEEE 104(1), 11\u201333 (2016)","journal-title":"Proc. IEEE"},{"key":"40_CR25","doi-asserted-by":"crossref","unstructured":"Perozzi, B., Al-Rfou, R., Skiena, S.: DeepWalk: online learning of social representations. ACM (2014)","DOI":"10.1145\/2623330.2623732"},{"key":"40_CR26","unstructured":"Ribeiro, L.F.R., Saverese, P.H.P., Figueiredo, D.R.: Struc2vec: learning node representations from structural identity. ACM (2017)"},{"key":"40_CR27","doi-asserted-by":"crossref","unstructured":"Shapley, L.S.: A Value for $$n$$-Person Games. Annals of Mathematical Studies (1953)","DOI":"10.1515\/9781400881970-018"},{"key":"40_CR28","unstructured":"Shchur, O., Mumme, M., Bojchevski, A., G\u00fcnnemann, S.: Pitfalls of graph neural network evaluation (2018)"},{"key":"40_CR29","doi-asserted-by":"crossref","unstructured":"Tang, J., Qu, M., Wang, M., Zhang, M., Mei, Q.: LINE: large-scale information network embedding (2015)","DOI":"10.1145\/2736277.2741093"},{"key":"40_CR30","unstructured":"Vaswani, A., et al.: Attention is all you need. arXiv (2017)"},{"key":"40_CR31","doi-asserted-by":"crossref","unstructured":"Wang, H., Lian, D., Zhang, Y., Qin, L., Lin, X.: GoGNN: graph of graphs neural network for predicting structured entity interactions (2020)","DOI":"10.24963\/ijcai.2020\/183"},{"issue":"3","key":"40_CR32","doi-asserted-by":"publisher","first-page":"538","DOI":"10.1109\/TMC.2014.2322373","volume":"14","author":"Z Wang","year":"2016","unstructured":"Wang, Z., Liao, J., Cao, Q., Qi, H., Wang, Z.: FriendBook: a semantic-based friend recommendation system for social networks. IEEE Trans. Mob. Comput. 14(3), 538\u2013551 (2016)","journal-title":"IEEE Trans. Mob. Comput."},{"key":"40_CR33","first-page":"1","volume":"32","author":"Z Wang","year":"2020","unstructured":"Wang, Z., Lei, Y., Li, W.: Neighborhood attention networks with adversarial learning for link prediction. IEEE Trans. Neural Netw. Learn. Syst. 32, 1\u201311 (2020)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"40_CR34","unstructured":"Yang, M., et al.: Hyperbolic graph neural networks: a review of methods and applications (2022)"},{"key":"40_CR35","unstructured":"Yang, Z., Cohen, W.W., Salakhutdinov, R.: Revisiting semi-supervised learning with graph embeddings. JMLR.org (2016)"},{"key":"40_CR36","doi-asserted-by":"crossref","unstructured":"Yuan, M., Liu, Q., Wang, G., Guo, Y.: HNECV: heterogeneous network embedding via cloud model and variational inference. In: CAAI International Conference on Artificial Intelligence. CAAI International Conference on Artificial Intelligence (2021)","DOI":"10.1007\/978-3-030-93046-2_63"},{"key":"40_CR37","unstructured":"Yun, S., Kim, S., Lee, J., Kang, J., Kim, H.: Neo-GNNs: neighborhood overlap-aware graph neural networks for link prediction (2022)"},{"key":"40_CR38","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1007\/978-0-387-39940-9_482","volume-title":"Encyclopedia of Database Systems","author":"E Zhang","year":"2009","unstructured":"Zhang, E., Zhang, Y.: Average precision. In: Liu, L., \u00d6zsu, M.T. (eds.) Encyclopedia of Database Systems, pp. 192\u2013193. Springer, Boston (2009). https:\/\/doi.org\/10.1007\/978-0-387-39940-9_482"},{"key":"40_CR39","unstructured":"Zhang, M., Chen, Y.: Link prediction based on graph neural networks (2018)"},{"issue":"2","key":"40_CR40","first-page":"5","volume":"38","author":"Y Zhang","year":"2019","unstructured":"Zhang, Y., Feng, Y.: A summary of the methods and development of link prediction (in Chinese). Meas. Control Technol. 38(2), 5 (2019)","journal-title":"Meas. Control Technol."},{"key":"40_CR41","unstructured":"Zhou, T., L\u00fc, L., Zhang, Y.: EPJ manuscript no. (will be inserted by the editor) predicting missing links via local information (2012)"}],"container-title":["Lecture Notes in Computer Science","Rough Sets"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-50959-9_40","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,30]],"date-time":"2023-12-30T10:07:37Z","timestamp":1703930857000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-50959-9_40"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031509582","9783031509599"],"references-count":41,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-50959-9_40","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"31 December 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IJCRS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Joint Conference on Rough Sets","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Krakow","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Poland","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":"5 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ijcrs2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/ijcrs2023.agh.edu.pl\/","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":"Springer EquinOCS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"83","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":"43","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":"0","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":"52% - 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":"5","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)"}}]}}