{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T12:50:48Z","timestamp":1777726248419,"version":"3.51.4"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2020,8,10]],"date-time":"2020-08-10T00:00:00Z","timestamp":1597017600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,8,10]],"date-time":"2020-08-10T00:00:00Z","timestamp":1597017600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"crossref","award":["2018YFB1004600"],"award-info":[{"award-number":["2018YFB1004600"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]},{"name":"National Science and Technology Major Project of China","award":["2017ZX05036-001"],"award-info":[{"award-number":["2017ZX05036-001"]}]},{"DOI":"10.13039\/501100001809","name":"NSFC","doi-asserted-by":"crossref","award":["61972365"],"award-info":[{"award-number":["61972365"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"NSFC","doi-asserted-by":"crossref","award":["61772480"],"award-info":[{"award-number":["61772480"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"NSFC","doi-asserted-by":"crossref","award":["61672474"],"award-info":[{"award-number":["61672474"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"NSFC","doi-asserted-by":"crossref","award":["61673354"],"award-info":[{"award-number":["61673354"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"NSFC","doi-asserted-by":"crossref","award":["61501412"],"award-info":[{"award-number":["61501412"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Mobile Netw Appl"],"published-print":{"date-parts":[[2022,2]]},"DOI":"10.1007\/s11036-020-01633-0","type":"journal-article","created":{"date-parts":[[2020,8,10]],"date-time":"2020-08-10T15:03:44Z","timestamp":1597071824000},"page":"315-328","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Graph Neural Networks with Information Anchors for Node Representation Learning"],"prefix":"10.1007","volume":"27","author":[{"given":"Chao","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinchuan","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dongyang","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shaolong","family":"Guo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5628-0545","authenticated-orcid":false,"given":"Xiaojun","family":"Kang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lijun","family":"Dong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hong","family":"Yao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,8,10]]},"reference":[{"issue":"1","key":"1633_CR1","first-page":"362","volume":"45","author":"D Szklarczyk","year":"2016","unstructured":"Szklarczyk D, Morris JH, Cook H, Kuhn M, Wyder S, Simonovic M, Santos A, Doncheva NT, Roth A, Bork P, et al. (2016) The string database in 2017: quality-controlled protein\u2013protein association networks, made broadly accessible. Nucleic acids research 45(1):362\u2013368","journal-title":"Nucleic acids research"},{"key":"1633_CR2","doi-asserted-by":"crossref","unstructured":"Ying R, He R, Chen K, Eksombatchai P, Hamilton WL, Leskovec J (2018) Graph convolutional neural networks for web-scale recommender systems. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining ACM, pp 974\u2013983","DOI":"10.1145\/3219819.3219890"},{"issue":"14","key":"1633_CR3","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1093\/bioinformatics\/btx252","volume":"33","author":"M Zitnik","year":"2017","unstructured":"Zitnik M, Leskovec J (2017) Predicting multicellular function through multi-layer tissue networks. Bioinformatics 33(14):190\u2013198","journal-title":"Bioinformatics"},{"key":"1633_CR4","unstructured":"Kipf TN, Welling M (2016) Semi-supervised classification with graph convolutional networks. arXiv:1609.02907"},{"key":"1633_CR5","doi-asserted-by":"crossref","unstructured":"Grover A, Leskovec J (2016) node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining ACM, pp 855\u2013864","DOI":"10.1145\/2939672.2939754"},{"key":"1633_CR6","unstructured":"Sun M, Tang J, Li H, Li B, Xiao C, Chen Y, Song D (2018) Data poisoning attack against unsupervised node embedding methods. arXiv:1810.12881"},{"key":"1633_CR7","doi-asserted-by":"crossref","unstructured":"Viviana AP, Cerquitelli T (2020) Modeling urban traffic data through graph-based neural networks Springer. In: INNS Big Data and Deep Learning conference, pp 216\u2013225","DOI":"10.1007\/978-3-030-16841-4_23"},{"key":"1633_CR8","doi-asserted-by":"crossref","unstructured":"Changbo Z, Zhang Q, Gao Z, Niu Z, Zheng N, Wang L, Hua G (2020) Action co-localization in an untrimmed video by graph neural networks Springer. In: INNS Big Data and Deep Learning conference, pp 555\u2013567","DOI":"10.1007\/978-3-030-37731-1_45"},{"key":"1633_CR9","doi-asserted-by":"crossref","unstructured":"Haddad PLDBM, Bothorel C (2020) Temporalnode2vec: Temporal node embedding in temporal networks Springer. In: International Conference on Complex Networks and Their Applications, pp 891\u2013902","DOI":"10.1007\/978-3-030-36687-2_74"},{"key":"1633_CR10","doi-asserted-by":"crossref","unstructured":"Sun G, Zhang X (2019) A novel framework for node\/edge attributed graph embedding Springer. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp 169\u2013182","DOI":"10.1007\/978-3-030-16142-2_14"},{"key":"1633_CR11","unstructured":"Yang C, Liu Z, Zhao D, Sun M, Chang E (2015) Network representation learning with rich text information. In: Twenty-Fourth International Joint Conference on Artificial Intelligence"},{"key":"1633_CR12","doi-asserted-by":"crossref","unstructured":"Chen Y, Zou L, Qin Z (2019) Gated relational graph neural network for semi-supervised learning on knowledge graphs. In: International Conference on Web Information Systems Engineering Springer, pp 617\u2013629","DOI":"10.1007\/978-3-030-34223-4_39"},{"key":"1633_CR13","doi-asserted-by":"crossref","unstructured":"Nasrullah Sheikh Z TK, Montresor A (2020) A simple approach to attributed graph embedding via enhanced autoencoder. In: International Conference on Complex Networks and Their Applications Springer, pp 797\u2013809","DOI":"10.1007\/978-3-030-36687-2_66"},{"issue":"1","key":"1633_CR14","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1109\/TNN.2008.2005605","volume":"20","author":"F Scarselli","year":"2008","unstructured":"Scarselli F, Gori M, Tsoi AC, Hagenbuchner M, Monfardini G (2008) The graph neural network model. IEEE Transactions on Neural Networks 20(1):61\u201380","journal-title":"IEEE Transactions on Neural Networks"},{"key":"1633_CR15","doi-asserted-by":"crossref","unstructured":"Xu N, Wang P, Chen L, Tao J, Zhao J (2019) Mr-gnn: Multi-resolution and dual graph neural network for predicting structured entity interactions. In: Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI-19), pp 3968\u20133974","DOI":"10.24963\/ijcai.2019\/551"},{"key":"1633_CR16","unstructured":"Jaume G, Nguyen A-, Mart\u00ednez MR, Thiran J-P, Gabrani M (2019) edgnn: a simple and powerful gnn for directed labeled graphs. arXiv:1904.08745"},{"key":"1633_CR17","doi-asserted-by":"crossref","unstructured":"Zheng Y, Jiang B, Shi J, Zhang H, Xie F (2019) Encoding histopathological wsis using gnn for scalable diagnostically relevant regions retrieval. In: International Conference on Medical Image Computing and Computer-Assisted Intervention Springer, pp 550\u2013558","DOI":"10.1007\/978-3-030-32239-7_61"},{"key":"1633_CR18","unstructured":"You J, Ying R, Leskovec J (2019) Position-aware graph neural networks. In: International Conference on Machine Learning, pp 7134\u20137143"},{"key":"1633_CR19","unstructured":"Veli\u010dkovi\u0107 P, Cucurull G, Casanova A, Romero A, Lio P, Bengio Y (2018) Graph attention networks. In: Sixth International Conference on Learning Representations (ICLR-18), pp 1\u201312"},{"key":"1633_CR20","unstructured":"Battaglia PW, Hamrick JB, Bapst V, Sanchez-Gonzalez A, Zambaldi V, Malinowski M, Tacchetti A, Raposo D, Santoro A, Faulkner R et al (2018) Relational inductive biases, deep learning, and graph networks. arXiv:1806.01261"},{"key":"1633_CR21","doi-asserted-by":"crossref","unstructured":"Li J-H, Wang C-D, Huang L, Huang D, Lai J-H, Chen P (2018) Attributed network embedding with micro-meso structure. In: International Conference on Database Systems for Advanced Applications Springer, pp 20\u201336","DOI":"10.1007\/978-3-319-91452-7_2"},{"key":"1633_CR22","doi-asserted-by":"crossref","first-page":"107000","DOI":"10.1016\/j.patcog.2019.107000","volume":"97","author":"F Manessi","year":"2020","unstructured":"Manessi F, Rozza A, Manzo M (2020) Dynamic graph convolutional networks. Pattern Recogn 97:107000","journal-title":"Pattern Recogn"},{"key":"1633_CR23","unstructured":"Hamilton W, Ying Z, Leskovec J (2017) Inductive representation learning on large graphs. In: Advances in Neural Information Processing Systems, pp 1024\u20131034"},{"key":"1633_CR24","unstructured":"Zhang M, Chen Y (2018) Link prediction based on graph neural networks. In: Advances in Neural Information Processing Systems, pp 5165\u20135175"},{"issue":"5","key":"1633_CR25","doi-asserted-by":"crossref","first-page":"1437","DOI":"10.1109\/TNNLS.2019.2920267","volume":"31","author":"C Zheng","year":"2019","unstructured":"Zheng C, Pan L, Wu P (2019) Multimodal deep network embedding with integrated structure and attribute information. IEEE Transactions on Neural Networks 31(5):1437\u20131449","journal-title":"IEEE Transactions on Neural Networks"},{"key":"1633_CR26","unstructured":"Liu XF, Tse CK (2015) A general framework for complex network applications. In: 2015 International Symposium on Nonlinear Theory and Its Applications (NOLTA2015), pp 2\u20135"},{"key":"1633_CR27","doi-asserted-by":"crossref","first-page":"61874","DOI":"10.1109\/ACCESS.2019.2916172","volume":"7","author":"X Ai","year":"2019","unstructured":"Ai X (2019) New metrics for node importance evaluation in occupational injury network. IEEE Access 7:61874\u201361882","journal-title":"IEEE Access"},{"key":"1633_CR28","doi-asserted-by":"crossref","unstructured":"Qiong Q, Dongxia W (2016) Evaluation method for node importance in complex networks based on eccentricity of node. In: 2016 2nd IEEE International Conference on Computer and Communications (ICCC) IEEE , pp 2499\u20132502","DOI":"10.1109\/CompComm.2016.7925149"},{"key":"1633_CR29","doi-asserted-by":"crossref","unstructured":"Zhao J, Liu X, Guo J (2018) Evaluation method for node importance of communication network based on complex network analysis. In: International Conference in Communications, Signal Processing, and Systems Springer, pp 342\u2013349","DOI":"10.1007\/978-981-13-6264-4_42"},{"key":"1633_CR30","doi-asserted-by":"crossref","first-page":"105464","DOI":"10.1016\/j.knosys.2019.105464","volume":"193","author":"F Liu","year":"2020","unstructured":"Liu F, Wang Z, Deng Y (2020) Gmm: A generalized mechanics model for identifying the importance of nodes in complex networks. Knowledge Based Systems 193:105464","journal-title":"Knowledge Based Systems"},{"key":"1633_CR31","doi-asserted-by":"crossref","first-page":"434","DOI":"10.1016\/j.asoc.2018.03.014","volume":"67","author":"A Moayedikia","year":"2018","unstructured":"Moayedikia A (2018) Multi-objective community detection algorithm with node importance analysis in attributed networks. Appl Soft Comput 67:434\u2013451","journal-title":"Appl Soft Comput"},{"issue":"3","key":"1633_CR32","doi-asserted-by":"crossref","first-page":"437","DOI":"10.1109\/TCSII.2018.2845940","volume":"66","author":"J Zhou","year":"2019","unstructured":"Zhou J, Yu X, Lu J (2019) Node importance in controlled complex networks. IEEE Transactions on Circuits and Systems Ii-express Briefs 66(3):437\u2013441","journal-title":"IEEE Transactions on Circuits and Systems Ii-express Briefs"},{"key":"1633_CR33","doi-asserted-by":"crossref","unstructured":"Zhang Y, Xiong Y, Kong X, Li S, Mi J, Zhu Y (2018) Deep collective classification in heterogeneous information networks. In: Proceedings of the 2018 World Wide Web Conference International World Wide Web Conferences Steering Committee, pp 399\u2013408","DOI":"10.1145\/3178876.3186106"},{"key":"1633_CR34","doi-asserted-by":"crossref","unstructured":"Beck D, Haffari G, Cohn T (2018) Graph-to-sequence learning using gated graph neural networks. In: Annual Meeting of the Association for Computational Linguistics 2018 Association for Computational Linguistics (ACL), pp 273\u2013283","DOI":"10.18653\/v1\/P18-1026"},{"key":"1633_CR35","doi-asserted-by":"crossref","first-page":"105548","DOI":"10.1016\/j.knosys.2020.105548","volume":"194","author":"Y Xie","year":"2020","unstructured":"Xie Y, Xu H, Li J, Yang C, Gao K (2020) Heterogeneous graph neural networks for noisy few-shot relation classification. Knowledge Based Systems 194:105548","journal-title":"Knowledge Based Systems"},{"key":"1633_CR36","doi-asserted-by":"crossref","unstructured":"Schlichtkrull M, Kipf TN, Bloem P, Van Den Berg R, Titov I, Welling M (2018) Modeling relational data with graph convolutional networks. In: European Semantic Web Conference Springer, pp 593\u2013607","DOI":"10.1007\/978-3-319-93417-4_38"},{"key":"1633_CR37","unstructured":"Duvenaud DK, Maclaurin D, Iparraguirre J, Bombarell R, Hirzel T, Aspuru-Guzik A, Adams RP (2015) Convolutional networks on graphs for learning molecular fingerprints. In: Advances in neural information processing systems, pp 2224\u20132232"},{"issue":"1","key":"1633_CR38","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1145\/1217299.1217301","volume":"1","author":"J Leskovec","year":"2007","unstructured":"Leskovec J, Kleinberg J, Faloutsos C (2007) Graph evolution: Densification and shrinking diameters. ACM Transactions on Knowledge Discovery from Data (TKDD) 1(1):2","journal-title":"ACM Transactions on Knowledge Discovery from Data (TKDD)"},{"key":"1633_CR39","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1093\/bioinformatics\/bti1007","volume":"21","author":"KM Borgwardt","year":"2005","unstructured":"Borgwardt KM, Ong CS, Sch\u00f6nauer S, Vishwanathan SVN, Smola AJ, Kriegel H-P (2005) Protein function prediction via graph kernels. Bioinformatics 21:47\u201356","journal-title":"Bioinformatics"},{"issue":"2","key":"1633_CR40","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1086\/210318","volume":"105","author":"DJ Watts","year":"1999","unstructured":"Watts DJ (1999) Networks, dynamics, and the small-world phenomenon. American Journal of sociology 105(2):493\u2013527","journal-title":"American Journal of sociology"},{"key":"1633_CR41","unstructured":"Xu K, Hu W, Leskovec J, Jegelka S (2019) How powerful are graph neural networks?. In: International Conference on Learning Representations, pp 1\u201317"}],"container-title":["Mobile Networks and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11036-020-01633-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11036-020-01633-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11036-020-01633-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,3,25]],"date-time":"2022-03-25T17:58:37Z","timestamp":1648231117000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11036-020-01633-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,10]]},"references-count":41,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,2]]}},"alternative-id":["1633"],"URL":"https:\/\/doi.org\/10.1007\/s11036-020-01633-0","relation":{},"ISSN":["1383-469X","1572-8153"],"issn-type":[{"value":"1383-469X","type":"print"},{"value":"1572-8153","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,8,10]]},"assertion":[{"value":"10 August 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}