{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T22:46:00Z","timestamp":1783550760068,"version":"3.55.0"},"publisher-location":"Cham","reference-count":35,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030865191","type":"print"},{"value":"9783030865207","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-86520-7_24","type":"book-chapter","created":{"date-parts":[[2021,9,9]],"date-time":"2021-09-09T15:25:48Z","timestamp":1631201148000},"page":"383-398","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Inductive Link Prediction with\u00a0Interactive Structure Learning on\u00a0Attributed Graph"],"prefix":"10.1007","author":[{"given":"Shuo","family":"Yang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Binbin","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiqiang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wang","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongyu","family":"Shan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuetian","family":"Cao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Borui","family":"Ye","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanming","family":"Fang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Quan","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,9,10]]},"reference":[{"key":"24_CR1","doi-asserted-by":"crossref","unstructured":"AbuOda, G., Morales, G.D.F., Aboulnaga, A.: Link prediction via higher-order motif features. In: ECML PKDD, pp. 412\u2013429 (2019)","DOI":"10.1007\/978-3-030-46150-8_25"},{"issue":"18","key":"24_CR2","doi-asserted-by":"publisher","first-page":"3825","DOI":"10.1016\/j.comnet.2012.10.007","volume":"56","author":"S Brin","year":"2012","unstructured":"Brin, S., Page, L.: Reprint of: the anatomy of a large-scale hypertextual web search engine. Comput. Netw. 56(18), 3825\u20133833 (2012)","journal-title":"Comput. Netw."},{"issue":"5","key":"24_CR3","doi-asserted-by":"publisher","first-page":"833","DOI":"10.1109\/TKDE.2018.2849727","volume":"31","author":"P Cui","year":"2018","unstructured":"Cui, P., Wang, X., Pei, J., Zhu, W.: A survey on network embedding. IEEE Trans. Knowl. Data Eng. 31(5), 833\u2013852 (2018)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"24_CR4","doi-asserted-by":"crossref","unstructured":"Feng, Y., Hu, B., Lv, F., Liu, Q., Zhang, Z., Ou, W.: ATBRG: adaptive target-behavior relational graph network for effective recommendation (2020)","DOI":"10.1145\/3397271.3401428"},{"key":"24_CR5","doi-asserted-by":"crossref","unstructured":"Grover, A., Leskovec, J.: node2vec: scalable feature learning for networks. In: SIGKDD, pp. 855\u2013864 (2016)","DOI":"10.1145\/2939672.2939754"},{"issue":"8","key":"24_CR6","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"key":"24_CR7","unstructured":"Hu, B., Hu, Z., Zhang, Z., Zhou, J., Shi, C.: KGNN: distributed framework for graph neural knowledge representation. In: ICML Workshop (2020)"},{"key":"24_CR8","doi-asserted-by":"crossref","unstructured":"Hu, B., Shi, C., Zhao, W.X., Yu, P.S.: Leveraging meta-path based context for top-n recommendation with a neural co-attention model. In: SIGKDD, pp. 1531\u20131540 (2018)","DOI":"10.1145\/3219819.3219965"},{"issue":"1","key":"24_CR9","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1007\/BF02289026","volume":"18","author":"L Katz","year":"1953","unstructured":"Katz, L.: A new status index derived from sociometric analysis. Psychometrika 18(1), 39\u201343 (1953)","journal-title":"Psychometrika"},{"issue":"8","key":"24_CR10","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"},{"issue":"1","key":"24_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41467-019-09177-y","volume":"10","author":"IA Kov\u00e1cs","year":"2019","unstructured":"Kov\u00e1cs, I.A., et al.: Network-based prediction of protein interactions. Nat. Commun. 10(1), 1\u20138 (2019)","journal-title":"Nat. Commun."},{"key":"24_CR12","doi-asserted-by":"publisher","first-page":"124289","DOI":"10.1016\/j.physa.2020.124289","volume":"553","author":"A Kumar","year":"2020","unstructured":"Kumar, A., Singh, S.S., Singh, K., Biswas, B.: Link prediction techniques, applications, and performance: A survey. Phys. A 553, 124289 (2020)","journal-title":"Phys. A"},{"issue":"7","key":"24_CR13","doi-asserted-by":"publisher","first-page":"1019","DOI":"10.1002\/asi.20591","volume":"58","author":"D Liben-Nowell","year":"2007","unstructured":"Liben-Nowell, D., Kleinberg, J.: The link-prediction problem for social networks. J. Am. Soc. Inform. Sci. Technol. 58(7), 1019\u20131031 (2007)","journal-title":"J. Am. Soc. Inform. Sci. Technol."},{"key":"24_CR14","doi-asserted-by":"crossref","unstructured":"Perozzi, B., Al-Rfou, R., Skiena, S.: DeepWalk: online learning of social representations. In: SIGKDD, pp. 701\u2013710 (2014)","DOI":"10.1145\/2623330.2623732"},{"key":"24_CR15","doi-asserted-by":"crossref","unstructured":"Sankar, A., Zhang, X., Chang, K.C.C.: Meta-GNN: metagraph neural network for semi-supervised learning in attributed heterogeneous information networks. In: ASONAM, pp. 137\u2013144 (2019)","DOI":"10.1145\/3341161.3342859"},{"key":"24_CR16","unstructured":"Sha, X., Sun, Z., Zhang, J.: Attentive knowledge graph embedding for personalized recommendation. arXiv preprint arXiv:1910.08288 (2019)"},{"key":"24_CR17","doi-asserted-by":"crossref","unstructured":"Shi, B., Weninger, T.: ProjE: embedding projection for knowledge graph completion. In: AAAI, vol. 31 (2017)","DOI":"10.1609\/aaai.v31i1.10677"},{"issue":"2","key":"24_CR18","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1109\/TKDE.2018.2833443","volume":"31","author":"C Shi","year":"2018","unstructured":"Shi, C., Hu, B., Zhao, W.X., Philip, S.Y.: Heterogeneous information network embedding for recommendation. IEEE Trans. Knowl. Data Eng. 31(2), 357\u2013370 (2018)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"1","key":"24_CR19","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1002\/asi.21664","volume":"63","author":"N Shibata","year":"2012","unstructured":"Shibata, N., Kajikawa, Y., Sakata, I.: Link prediction in citation networks. J. Am. Soc. Inform. Sci. Technol. 63(1), 78\u201385 (2012)","journal-title":"J. Am. Soc. Inform. Sci. Technol."},{"key":"24_CR20","unstructured":"Teru, K., Denis, E., Hamilton, W.: Inductive relation prediction by subgraph reasoning. In: ICML, pp. 9448\u20139457 (2020)"},{"key":"24_CR21","unstructured":"Veli\u010dkovi\u0107, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. arXiv preprint arXiv:1710.10903 (2017)"},{"key":"24_CR22","doi-asserted-by":"crossref","unstructured":"Wang, D., Cui, P., Zhu, W.: Structural deep network embedding. In: SIGKDD, pp. 1225\u20131234 (2016)","DOI":"10.1145\/2939672.2939753"},{"key":"24_CR23","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, pp. 1317\u20131323 (2020)","DOI":"10.24963\/ijcai.2020\/183"},{"issue":"3","key":"24_CR24","doi-asserted-by":"publisher","first-page":"538","DOI":"10.1109\/TMC.2014.2322373","volume":"14","author":"Z Wang","year":"2014","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 (2014)","journal-title":"IEEE Trans. Mob. Comput."},{"key":"24_CR25","doi-asserted-by":"crossref","unstructured":"Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE Trans. Neural Netw. Learn. Syst. 32, 4\u201324 (2020)","DOI":"10.1109\/TNNLS.2020.2978386"},{"key":"24_CR26","doi-asserted-by":"crossref","unstructured":"Wu, Z., Pan, S., Long, G., Jiang, J., Zhang, C.: Graph WaveNet for deep spatial-temporal graph modeling. arXiv preprint arXiv:1906.00121 (2019)","DOI":"10.24963\/ijcai.2019\/264"},{"key":"24_CR27","unstructured":"Xu, C., Cui, Z., Hong, X., Zhang, T., Yang, J., Liu, W.: Graph inference learning for semi-supervised classification. arXiv preprint arXiv:2001.06137 (2020)"},{"key":"24_CR28","doi-asserted-by":"crossref","unstructured":"Xu, N., Wang, P., Chen, L., Tao, J., Zhao, J.: MR-GNN: multi-resolution and dual graph neural network for predicting structured entity interactions, pp. 3968\u20133974 (2019)","DOI":"10.24963\/ijcai.2019\/551"},{"key":"24_CR29","doi-asserted-by":"crossref","unstructured":"Yang, S., et al.: Financial risk analysis for SMEs with graph-based supply chain mining. In: IJCAI, pp. 4661\u20134667 (2020)","DOI":"10.24963\/ijcai.2020\/643"},{"key":"24_CR30","doi-asserted-by":"crossref","unstructured":"Yang, S., Zou, L., Wang, Z., Yan, J., Wen, J.R.: Efficiently answering technical questions\u2013a knowledge graph approach. In: AAAI, vol. 31 (2017)","DOI":"10.1609\/aaai.v31i1.10956"},{"key":"24_CR31","unstructured":"Yang, Z., Cohen, W., Salakhudinov, R.: Revisiting semi-supervised learning with graph embeddings. In: ICML, pp. 40\u201348 (2016)"},{"key":"24_CR32","doi-asserted-by":"crossref","unstructured":"Ying, R., He, R., Chen, K., Eksombatchai, P., Hamilton, W.L., Leskovec, J.: Graph convolutional neural networks for web-scale recommender systems. In: SIGKDD, pp. 974\u2013983 (2018)","DOI":"10.1145\/3219819.3219890"},{"key":"24_CR33","unstructured":"Zhang, D., et al.: AGL: a scalable system for industrial-purpose graph machine learning. arXiv preprint arXiv:2003.02454 (2020)"},{"key":"24_CR34","unstructured":"Zhang, M., Chen, Y.: Link prediction based on graph neural networks. In: NeurIPS, pp. 5165\u20135175 (2018)"},{"key":"24_CR35","unstructured":"Zhang, W., Fang, Y., Liu, Z., Wu, M., Zhang, X.: mg2vec: learning relationship-preserving heterogeneous graph representations via metagraph embedding. IEEE Trans. Knowl. Data Eng. (2020)"}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases. Research Track"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-86520-7_24","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T22:05:38Z","timestamp":1757369138000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-86520-7_24"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030865191","9783030865207"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-86520-7_24","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"10 September 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Bilbao","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 September 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2021.ecmlpkdd.org\/","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":"869","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":"210","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":"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":"3-4","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":"3-9","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":"The conference was held online due to the COVID-19 pandemic.","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)"}}]}}