{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T17:45:19Z","timestamp":1785865519064,"version":"3.56.0"},"reference-count":74,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,11,2]],"date-time":"2021-11-02T00:00:00Z","timestamp":1635811200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,11,2]],"date-time":"2021-11-02T00:00:00Z","timestamp":1635811200000},"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":["Machine Vision and Applications"],"published-print":{"date-parts":[[2022,1]]},"DOI":"10.1007\/s00138-021-01251-0","type":"journal-article","created":{"date-parts":[[2021,11,2]],"date-time":"2021-11-02T17:51:20Z","timestamp":1635875480000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":166,"title":["Graph neural networks in node classification: survey and evaluation"],"prefix":"10.1007","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1187-8719","authenticated-orcid":false,"given":"Shunxin","family":"Xiao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shiping","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuanfei","family":"Dai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenzhong","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,11,2]]},"reference":[{"issue":"4","key":"1251_CR1","doi-asserted-by":"publisher","first-page":"541","DOI":"10.1162\/neco.1989.1.4.541","volume":"1","author":"Y LeCun","year":"1989","unstructured":"LeCun, Y., Boser, B., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W., Jackel, L.D.: Backpropagation applied to handwritten zip code recognition. Neural Comput. 1(4), 541\u2013551 (1989)","journal-title":"Neural Comput."},{"key":"1251_CR2","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Proceedings of the 26th Conference on Neural Information Processing Systems, pp. 1097\u20131105 (2012)"},{"key":"1251_CR3","doi-asserted-by":"crossref","unstructured":"Wan, W., Zhong, Y., Li, T., Chen, J.: Rethinking feature distribution for loss functions in image classification. In: Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition, pp. 9117\u20139126 (2018)","DOI":"10.1109\/CVPR.2018.00950"},{"key":"1251_CR4","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431\u20133440 (2015)","DOI":"10.1109\/CVPR.2015.7298965"},{"issue":"6","key":"1251_CR5","doi-asserted-by":"publisher","first-page":"684","DOI":"10.3390\/rs11060684","volume":"11","author":"M Papadomanolaki","year":"2019","unstructured":"Papadomanolaki, M., Vakalopoulou, M., Karantzalos, K.: A novel object-based deep learning framework for semantic segmentation of very high-resolution remote sensing data: Comparison with convolutional and fully convolutional networks. Remote Sens. 11(6), 684 (2019)","journal-title":"Remote Sens."},{"key":"1251_CR6","doi-asserted-by":"crossref","unstructured":"Chen, L., Zhang, H., Xiao, J., Nie, L., Shao, J., Liu, W., Chua, T.S.: Sca-cnn: Spatial and channel-wise attention in convolutional networks for image captioning. In: Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, pp. 5659\u20135667 (2017)","DOI":"10.1109\/CVPR.2017.667"},{"key":"1251_CR7","doi-asserted-by":"crossref","unstructured":"Aneja, J., Deshpande, A., Schwing, A.G.: Convolutional image captioning. In: Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition, pp. 5561\u20135570 (2018)","DOI":"10.1109\/CVPR.2018.00583"},{"issue":"2","key":"1251_CR8","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1207\/s15516709cog1402_1","volume":"14","author":"JL Elman","year":"1990","unstructured":"Elman, J.L.: Finding structure in time. Cogn. Sci. 14(2), 179\u2013211 (1990)","journal-title":"Cogn. Sci."},{"issue":"8","key":"1251_CR9","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":"1251_CR10","doi-asserted-by":"crossref","unstructured":"Tang, D., Qin, B., Liu, T.: Document modeling with gated recurrent neural network for sentiment classification. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 1422\u20131432 (2015)","DOI":"10.18653\/v1\/D15-1167"},{"key":"1251_CR11","doi-asserted-by":"crossref","unstructured":"Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive lstm. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence, pp. 5876\u20135883 (2018)","DOI":"10.1609\/aaai.v32i1.12048"},{"key":"1251_CR12","doi-asserted-by":"crossref","unstructured":"Liu, S., Yang, N., Li, M., Zhou, M.: A recursive recurrent neural network for statistical machine translation. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 1491\u20131500 (2014)","DOI":"10.3115\/v1\/P14-1140"},{"key":"1251_CR13","doi-asserted-by":"crossref","unstructured":"Su, J., Wu, S., Xiong, D., Lu, Y., Han, X., Zhang, B.: Variational recurrent neural machine translation. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence, pp. 5488\u20135495 (2018)","DOI":"10.1609\/aaai.v32i1.11985"},{"key":"1251_CR14","unstructured":"Xiong, C., Merity, S., Socher, R.: Dynamic memory networks for visual and textual question answering. In: Proceedings of the 33nd International Conference on Machine Learning, pp. 2397\u20132406 (2016)"},{"key":"1251_CR15","doi-asserted-by":"crossref","unstructured":"Lin, Y., Ji, H., Liu, Z., Sun, M.: Denoising distantly supervised open-domain question answering. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 1736\u20131745 (2018)","DOI":"10.18653\/v1\/P18-1161"},{"issue":"2","key":"1251_CR16","doi-asserted-by":"publisher","first-page":"3465","DOI":"10.1016\/j.eswa.2008.02.064","volume":"36","author":"M Karabatak","year":"2009","unstructured":"Karabatak, M., Ince, M.C.: An expert system for detection of breast cancer based on association rules and neural network. Expert Syst. Appl. 36(2), 3465\u20133469 (2009)","journal-title":"Expert Syst. Appl."},{"key":"1251_CR17","doi-asserted-by":"crossref","unstructured":"Cire\u015fan, D.C., Giusti, A., Gambardella, L.M., Schmidhuber, J.: Mitosis detection in breast cancer histology images with deep neural networks. In: Proceedings of the 16th International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 11\u2013418 (2013)","DOI":"10.1007\/978-3-642-40763-5_51"},{"issue":"1","key":"1251_CR18","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1142\/S0129065707000890","volume":"17","author":"A Panakkat","year":"2007","unstructured":"Panakkat, A., Adeli, H.: Neural network models for earthquake magnitude prediction using multiple seismicity indicators. Int. J. Neural Syst. 17(1), 13\u201333 (2007)","journal-title":"Int. J. Neural Syst."},{"issue":"7","key":"1251_CR19","doi-asserted-by":"publisher","first-page":"1018","DOI":"10.1016\/j.neunet.2009.05.003","volume":"22","author":"H Adeli","year":"2009","unstructured":"Adeli, H., Panakkat, A.: A probabilistic neural network for earthquake magnitude prediction. Neural Netw. 22(7), 1018\u20131024 (2009)","journal-title":"Neural Netw."},{"issue":"7587","key":"1251_CR20","doi-asserted-by":"publisher","first-page":"484","DOI":"10.1038\/nature16961","volume":"529","author":"D Silver","year":"2016","unstructured":"Silver, D., Huang, A., Maddison, C.J., Guez, A., Sifre, L., Van Den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al.: Mastering the game of go with deep neural networks and tree search. Nature. 529(7587), 484 (2016)","journal-title":"Nature."},{"key":"1251_CR21","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.neucom.2016.12.038","volume":"234","author":"W Liu","year":"2017","unstructured":"Liu, W., Wang, Z., Liu, X., Zeng, N., Liu, Y., Alsaadi, F.E.: A survey of deep neural network architectures and their applications. Neurocomputing. 234, 11\u201326 (2017)","journal-title":"Neurocomputing."},{"key":"1251_CR22","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1016\/j.inffus.2017.10.006","volume":"42","author":"Q Zhang","year":"2018","unstructured":"Zhang, Q., Yang, L.T., Chen, Z., Li, P.: A survey on deep learning for big data. Inf. Fusion. 42, 146\u2013157 (2018)","journal-title":"Inf. Fusion."},{"issue":"3","key":"1251_CR23","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1038\/nrn2575","volume":"10","author":"E Bullmore","year":"2009","unstructured":"Bullmore, E., Sporns, O.: Complex brain networks: graph theoretical analysis of structural and functional systems. Nat. Rev. Neurosci. 10(3), 186 (2009)","journal-title":"Nat. Rev. Neurosci."},{"key":"1251_CR24","doi-asserted-by":"crossref","unstructured":"Yang, J., Leskovec, J.: Community-affiliation graph model for overlapping network community detection. In: Proceedings of 12th IEEE International Conference on Data Mining, pp. 1170\u20131175 (2012)","DOI":"10.1109\/ICDM.2012.139"},{"key":"1251_CR25","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1016\/j.future.2018.10.045","volume":"94","author":"V Bhatia","year":"2019","unstructured":"Bhatia, V., Rani, R.: A distributed overlapping community detection model for large graphs using autoencoder. Futur. Gener. Comp. Syst. 94, 16\u201326 (2019)","journal-title":"Futur. Gener. Comp. Syst."},{"issue":"5","key":"1251_CR26","doi-asserted-by":"publisher","first-page":"2191","DOI":"10.1109\/TITS.2014.2311123","volume":"15","author":"W Huang","year":"2014","unstructured":"Huang, W., Song, G., Hong, H., Xie, K.: Deep architecture for traffic flow prediction: Deep belief networks with multitask learning. IEEE Trans. Intell. Transp. Syst. 15(5), 2191\u20132201 (2014)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"1251_CR27","doi-asserted-by":"publisher","first-page":"6755","DOI":"10.1109\/ACCESS.2017.2786217","volume":"6","author":"H Qi","year":"2017","unstructured":"Qi, H.: Graphical solution for arterial road traffic flow model considering spillover. IEEE Access. 6, 6755\u20136764 (2017)","journal-title":"IEEE Access."},{"key":"1251_CR28","doi-asserted-by":"crossref","unstructured":"Ji, G., Liu, K., He, S., Zhao, J.: Knowledge graph completion with adaptive sparse transfer matrix. In: Proceedings of the 30th AAAI Conference on Artificial Intelligence, pp. 985\u2013991 (2016)","DOI":"10.1609\/aaai.v30i1.10089"},{"key":"1251_CR29","doi-asserted-by":"crossref","unstructured":"Hamaguchi, T., Oiwa, H., Shimbo, M., Matsumoto, Y.: Knowledge transfer for out-of-knowledge-base entities: A graph neural network approach. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence, pp. 1802\u20131808 (2017)","DOI":"10.24963\/ijcai.2017\/250"},{"key":"1251_CR30","doi-asserted-by":"crossref","unstructured":"Gori, M., Monfardini, G., Scarselli, F.: A new model for learning in graph domains. In: Proceedings of the 2005 IEEE International Joint Conference on Neural Networks, pp. 729\u2013734 (2005)","DOI":"10.1109\/IJCNN.2005.1555942"},{"issue":"1","key":"1251_CR31","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1109\/TNN.2008.2005605","volume":"20","author":"F Scarselli","year":"2009","unstructured":"Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE Trans. Neural Netw. 20(1), 61\u201380 (2009)","journal-title":"IEEE Trans. Neural Netw."},{"key":"1251_CR32","unstructured":"Duvenaud, D.K., Maclaurin, D., Iparraguirre, J., Bombarell, R., Hirzel, T., Aspuru-Guzik, A., Adams, R.P.: Convolutional networks on graphs for learning molecular fingerprints. In: Proceedings of the 29th Conference on Neural Information Processing Systems, pp. 2224\u20132232 (2015)"},{"key":"1251_CR33","unstructured":"Li, Y., Tarlow, D., Brockschmidt, M., Zemel, R.S.: Gated graph sequence neural networks. In: Proceedings of the 4th International Conference on Learning Representations (2016)"},{"key":"1251_CR34","unstructured":"Atwood, J., Towsley, D.: Diffusion-convolutional neural networks. In: Proceedings of the 30th Conference on Neural Information Processing Systems, pp. 1993\u20132001 (2016)"},{"key":"1251_CR35","doi-asserted-by":"crossref","unstructured":"Li, Q., Han, Z., Wu, X.M.: Deeper insights into graph convolutional networks for semi-supervised learning. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence, pp. 3538\u20133545 (2018)","DOI":"10.1609\/aaai.v32i1.11604"},{"key":"1251_CR36","unstructured":"Zhang, M., Chen, Y.: Link prediction based on graph neural networks. In: Proceedings of the 32nd Conference on Neural Information Processing Systems, pp. 5165\u20135175 (2018)"},{"key":"1251_CR37","doi-asserted-by":"crossref","unstructured":"Xu, X., Liu, C., Feng, Q., Yin, H., Song, L., Song, D.: Neural network-based graph embedding for cross-platform binary code similarity detection. In: Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, pp. 363\u2013376 (2017)","DOI":"10.1145\/3133956.3134018"},{"key":"1251_CR38","unstructured":"Niepert, M., Ahmed, M., Kutzkov, K.: Learning convolutional neural networks for graphs. In: Proceedings of the 33rd International Conference on Machine Learning, pp. 2014\u20132023 (2016)"},{"key":"1251_CR39","doi-asserted-by":"crossref","unstructured":"Zhang, M., Cui, Z., Neumann, M., Chen, Y.: An end-to-end deep learning architecture for graph classification. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence, pp. 4438\u20134445 (2018)","DOI":"10.1609\/aaai.v32i1.11782"},{"issue":"1","key":"1251_CR40","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1016\/j.neucom.2011.04.047","volume":"75","author":"P Kazienko","year":"2012","unstructured":"Kazienko, P., Kajdanowicz, T.: Label-dependent node classification in the network. Neurocomputing. 75(1), 199\u2013209 (2012)","journal-title":"Neurocomputing."},{"issue":"3","key":"1251_CR41","first-page":"93","volume":"29","author":"P Sen","year":"2008","unstructured":"Sen, P., Namata, G., Bilgic, M., Getoor, L., Galligher, B., Eliassi-Rad, T.: Collective classification in network data. AI Mag. 29(3), 93\u201393 (2008)","journal-title":"AI Mag."},{"key":"1251_CR42","doi-asserted-by":"crossref","unstructured":"Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., Mei, Q.: 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"},{"issue":"43","key":"1251_CR43","doi-asserted-by":"publisher","first-page":"15545","DOI":"10.1073\/pnas.0506580102","volume":"102","author":"A Subramanian","year":"2005","unstructured":"Subramanian, A., Tamayo, P., Mootha, V.K., Mukherjee, S., Ebert, B.L., Gillette, M.A., Paulovich, A., Pomeroy, S.L., Golub, T.R., Lander, E.S., et al.: Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci USA 102(43), 15545\u201315550 (2005)","journal-title":"Proc. Natl. Acad. Sci USA"},{"issue":"22","key":"1251_CR44","doi-asserted-by":"publisher","first-page":"2800","DOI":"10.1093\/bioinformatics\/btl467","volume":"22","author":"J Xu","year":"2006","unstructured":"Xu, J., Li, Y.: Discovering disease-genes by topological features in human protein-protein interaction network. Bioinformatics. 22(22), 2800\u20132805 (2006)","journal-title":"Bioinformatics."},{"key":"1251_CR45","unstructured":"Defferrard, M., Bresson, X., Vandergheynst, P.: Convolutional neural networks on graphs with fast localized spectral filtering. In: Proceedings of the 30th Conference on Neural Information Processing Systems, pp. 3844\u20133852 (2016)"},{"key":"1251_CR46","doi-asserted-by":"crossref","unstructured":"Spielman, D.A.: Spectral graph theory and its applications. In: Proceedings of the 48th Annual IEEE Symposium on Foundations of Computer Science, pp. 29\u201338. IEEE (2007)","DOI":"10.1109\/FOCS.2007.56"},{"issue":"11","key":"1251_CR47","doi-asserted-by":"publisher","first-page":"1944","DOI":"10.1109\/TPAMI.2007.1115","volume":"29","author":"IS Dhillon","year":"2007","unstructured":"Dhillon, I.S., Guan, Y., Kulis, B.: Weighted graph cuts without eigenvectors a multilevel approach. IEEE Trans. Pattern Anal. Mach. Intell. 29(11), 1944\u20131957 (2007)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1251_CR48","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: Proceedings of the 4th International Conference on Learning Representations (2016)"},{"issue":"2","key":"1251_CR49","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1016\/j.acha.2010.04.005","volume":"30","author":"DK Hammond","year":"2011","unstructured":"Hammond, D.K., Vandergheynst, P., Gribonval, R.: Wavelets on graphs via spectral graph theory. Appl. Comput. Harmon. Anal. 30(2), 129\u2013150 (2011)","journal-title":"Appl. Comput. Harmon. Anal."},{"key":"1251_CR50","unstructured":"Hamilton, W., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st Conference on Neural Information Processing Systems, pp. 1024\u20131034 (2017)"},{"key":"1251_CR51","unstructured":"Xu, K., Li, C., Tian, Y., Sonobe, T., Kawarabayashi, K., Jegelka, S.: Representation learning on graphs with jumping knowledge networks. In: Proceedings of the 35th International Conference on Machine Learning, pp. 5449\u20135458 (2018)"},{"key":"1251_CR52","unstructured":"Klicpera, J., Bojchevski, A., G\u00fcnnemann, S.: Predict then propagate: Graph neural networks meet personalized pagerank. In: Proceedings of the 7th International Conference on Learning Representations (2018)"},{"key":"1251_CR53","unstructured":"Page, L., Brin, S., Motwani, R., Winograd, T.: The pagerank citation ranking: Bringing order to the web. Tech. rep, Stanford InfoLab (1999)"},{"key":"1251_CR54","doi-asserted-by":"crossref","unstructured":"Haveliwala, T.H.: Topic-sensitive pagerank. In: Proceedings of the 11st International Conference on World Wide Web, pp. 517\u2013526. ACM (2002)","DOI":"10.1145\/511446.511513"},{"key":"1251_CR55","unstructured":"Wu, F., Zhang, T., Souza, A.H.J., Fifty, C., Yu, T., Weinberger, K.Q.: Simplifying graph convolutional networks. In: Proceedings of the 36th International Conference on Machine Learning, pp. 6861\u20136871 (2019)"},{"key":"1251_CR56","doi-asserted-by":"crossref","unstructured":"Bottou, L.: Large-scale machine learning with stochastic gradient descent. In: Proceedings of the 19th International Conference on Computational Statistics, pp. 177\u2013186 (2010)","DOI":"10.1007\/978-3-7908-2604-3_16"},{"key":"1251_CR57","unstructured":"Veli\u010dkovi\u0107, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. In: Proceedings of the 5th International Conference on Learning Representations (2017)"},{"key":"1251_CR58","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st Conference on Neural Information Processing Systems, pp. 5998\u20136008 (2017)"},{"key":"1251_CR59","unstructured":"Thekumparampil, K.K., Wang, C., Oh, S., Li, L.J.: Attention-based graph neural network for semi-supervised learning (2018). arXiv:1803.03735"},{"key":"1251_CR60","unstructured":"Duan, Y., Andrychowicz, M., Stadie, B., Ho, O.J., Schneider, J., Sutskever, I., Abbeel, P., Zaremba, W.: One-shot imitation learning. In: Proceedings of the 31st Conference on Neural Information Processing Systems, pp. 1087\u20131098 (2017)"},{"key":"1251_CR61","unstructured":"Hoshen, Y.: Vain: Attentional multi-agent predictive modeling. In: Proceedings of the 31st Conference on Neural Information Processing Systems, pp. 2701\u20132711 (2017)"},{"key":"1251_CR62","unstructured":"Kipf, T.N., Welling, M.: Variational graph auto-encoders (2016). arXiv:1611.07308"},{"key":"1251_CR63","unstructured":"Kingma, D.P., Welling, M.: Auto-encoding variational bayes. In: Proceedings of the 2nd International Conference on Learning Representations (2013)"},{"key":"1251_CR64","doi-asserted-by":"crossref","unstructured":"He, S., Liu, K., Ji, G., Zhao, J.: Learning to represent knowledge graphs with gaussian embedding. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, pp. 623\u2013632 (2015)","DOI":"10.1145\/2806416.2806502"},{"key":"1251_CR65","unstructured":"Bojchevski, A., G\u00fcnnemann, S.: Deep gaussian embedding of graphs: Unsupervised inductive learning via ranking. In: Proceedings of the 5th International Conference on Learning Representations (2017)"},{"key":"1251_CR66","doi-asserted-by":"crossref","unstructured":"Dos\u00a0Santos, L., Piwowarski, B., Gallinari, P.: Multilabel classification on heterogeneous graphs with gaussian embeddings. In: Proceedings of the 2016 Joint European Conference on Machine Learning and knowledge Discovery in Databases, pp. 606\u2013622 (2016)","DOI":"10.1007\/978-3-319-46227-1_38"},{"key":"1251_CR67","doi-asserted-by":"crossref","unstructured":"Ribeiro, L.F., Saverese, P.H., Figueiredo, D.R.: Struc2vec: Learning node representations from structural identity. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 385\u2013394 (2017)","DOI":"10.1145\/3097983.3098061"},{"key":"1251_CR68","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"},{"key":"1251_CR69","unstructured":"Veli\u010dkovi\u0107, P., Fedus, W., Hamilton, W.L., Li\u00f2, P., Bengio, Y., Hjelm, R.D.: Deep graph infomax. In: Proceedings of the 6th International Conference on Learning Representations (2018)"},{"key":"1251_CR70","unstructured":"Yang, Z., Cohen, W.W., Salakhutdinov, R.: Revisiting semi-supervised learning with graph embeddings. In: Proceedings of the 33rd International Conference on Machine Learning, pp. 40\u201348 (2016)"},{"key":"1251_CR71","unstructured":"Pan, S., Wu, J., Zhu, X., Zhang, C., Wang, Y.: Tri-party deep network representation. In: Proceedings of the 25th International Joint Conference on Artificial Intelligence, pp. 1895\u20131901 (2016)"},{"key":"1251_CR72","doi-asserted-by":"crossref","unstructured":"McAuley, J., Targett, C., Shi, Q., Van Den\u00a0Hengel, A.: Image-based recommendations on styles and substitutes. In: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 43\u201352 (2015)","DOI":"10.1145\/2766462.2767755"},{"issue":"14","key":"1251_CR73","doi-asserted-by":"publisher","first-page":"i190","DOI":"10.1093\/bioinformatics\/btx252","volume":"33","author":"M Zitnik","year":"2017","unstructured":"Zitnik, M., Leskovec, J.: Predicting multicellular function through multi-layer tissue networks. Bioinformatics. 33(14), i190\u2013i198 (2017)","journal-title":"Bioinformatics."},{"key":"1251_CR74","unstructured":"Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Proceedings of the 4th International Conference on Learning Representations (2014)"}],"container-title":["Machine Vision and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00138-021-01251-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00138-021-01251-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00138-021-01251-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T09:18:35Z","timestamp":1726046315000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00138-021-01251-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,2]]},"references-count":74,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,1]]}},"alternative-id":["1251"],"URL":"https:\/\/doi.org\/10.1007\/s00138-021-01251-0","relation":{},"ISSN":["0932-8092","1432-1769"],"issn-type":[{"value":"0932-8092","type":"print"},{"value":"1432-1769","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,2]]},"assertion":[{"value":"27 October 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 September 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 September 2021","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 November 2021","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"4"}}