{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T03:21:14Z","timestamp":1740108074994,"version":"3.37.3"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2023,1,20]],"date-time":"2023-01-20T00:00:00Z","timestamp":1674172800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,20]],"date-time":"2023-01-20T00:00:00Z","timestamp":1674172800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100012166","name":"national key r &d program of china","doi-asserted-by":"crossref","award":["2018YFB1402600"],"award-info":[{"award-number":["2018YFB1402600"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"national natural science foundation of china","doi-asserted-by":"publisher","award":["61772083","61802028"],"award-info":[{"award-number":["61772083","61802028"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"DOI":"10.1007\/s00521-022-08175-4","type":"journal-article","created":{"date-parts":[[2023,1,20]],"date-time":"2023-01-20T21:38:41Z","timestamp":1674250721000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multi-level self-adaptive prototypical networks for few-shot node classification on attributed networks"],"prefix":"10.1007","author":[{"given":"Xin","family":"Xu","sequence":"first","affiliation":[]},{"given":"Junping","family":"Du","sequence":"additional","affiliation":[]},{"given":"Zhe","family":"Xue","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,1,20]]},"reference":[{"key":"8175_CR1","doi-asserted-by":"publisher","first-page":"i47","DOI":"10.1093\/bioinformatics\/bti1007","volume":"21","author":"KM Borgwardt","year":"2005","unstructured":"Borgwardt KM, Ong CS, Sch\u00f6nauer S, Vishwanathan S, Smola AJ, Kriegel HP (2005) Protein function prediction via graph kernels. Bioinformatics 21:i47\u2013i56","journal-title":"Bioinformatics"},{"key":"8175_CR2","unstructured":"Bruna J, Zaremba W, Szlam A, Lecun Y (2013) Spectral networks and locally connected networks on graphs. http:\/\/arxiv.org\/abs\/1312.6203"},{"key":"8175_CR3","doi-asserted-by":"crossref","unstructured":"Cao S, Lu W, Xu Q (2015) Grarep: Learning graph representations with global structural information. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, pp. 891\u2013900","DOI":"10.1145\/2806416.2806512"},{"key":"8175_CR4","unstructured":"Chen J, Ma T, Xiao C (2018) Fastgcn: fast learning with graph convolutional networks via importance sampling. http:\/\/arxiv.org\/abs\/1801.10247"},{"key":"8175_CR5","doi-asserted-by":"crossref","unstructured":"Cohen-Shapira N, Rokach L, Shapira B, Katz G, Vainshtein R (2019) Autogrd: Model recommendation through graphical dataset representation. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management,pp. 821\u2013830","DOI":"10.1145\/3357384.3357896"},{"key":"8175_CR6","first-page":"3837","volume":"25","author":"M Defferrard","year":"2016","unstructured":"Defferrard M, Bresson X, Vandergheynst P (2016) Convolutional neural networks on graphs with fast localized spectral filtering. Adv Neural Inf Process Syst 25:3837\u20133845","journal-title":"Adv Neural Inf Process Syst"},{"key":"8175_CR7","doi-asserted-by":"crossref","unstructured":"Ding K, Li J, Agarwal N, Liu H (2020) Inductive anomaly detection on attributed networks. In: 29th International Joint Conference on Artificial Intelligence, IJCAI 2020, pp. 1288\u20131294. International Joint Conferences on Artificial Intelligence","DOI":"10.24963\/ijcai.2020\/179"},{"key":"8175_CR8","doi-asserted-by":"crossref","unstructured":"Ding K, Li J, Bhanushali R, Liu H (2019) Deep anomaly detection on attributed networks. In: Proceedings of the 2019 SIAM International Conference on Data Mining, pp. 594\u2013602. SIAM","DOI":"10.1137\/1.9781611975673.67"},{"key":"8175_CR9","doi-asserted-by":"crossref","unstructured":"Ding K, Wang J, Li J, Shu K, Liu C, Liu H (2020) Graph prototypical networks for few-shot learning on attributed networks. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp. 295\u2013304","DOI":"10.1145\/3340531.3411922"},{"key":"8175_CR10","unstructured":"Finn C, Abbeel P, Levine S (2017) Model-agnostic meta-learning for fast adaptation of deep networks. In: International Conference on Machine Learning, pp. 1126\u20131135. PMLR"},{"key":"8175_CR11","doi-asserted-by":"crossref","unstructured":"Gao H, Huang H (2018) Deep attributed network embedding. In: Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI))","DOI":"10.24963\/ijcai.2018\/467"},{"key":"8175_CR12","doi-asserted-by":"crossref","unstructured":"Gao H, Wang Z, Ji S (2018) Large-scale learnable graph convolutional networks. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1416\u20131424","DOI":"10.1145\/3219819.3219947"},{"key":"8175_CR13","unstructured":"Garcia V, Bruna J (2017) Few-shot learning with graph neural networks. http:\/\/arxiv.org\/abs\/1711.04043"},{"key":"8175_CR14","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, pp. 855\u2013864","DOI":"10.1145\/2939672.2939754"},{"key":"8175_CR15","unstructured":"Hamilton W.L, Ying R, Leskovec J (2017) Inductive representation learning on large graphs. http:\/\/arxiv.org\/abs\/1706.02216"},{"key":"8175_CR16","doi-asserted-by":"crossref","unstructured":"Huang X, Li J, Hu X (2017) Label informed attributed network embedding. In: Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, pp. 731\u2013739","DOI":"10.1145\/3018661.3018667"},{"key":"8175_CR17","doi-asserted-by":"crossref","unstructured":"Joshi V, Peters M, Hopkins M (2018) Extending a parser to distant domains using a few dozen partially annotated examples. http:\/\/arxiv.org\/abs\/1805.06556","DOI":"10.18653\/v1\/P18-1110"},{"key":"8175_CR18","unstructured":"Kaise, \u0141, Nachum O, Roy A, Bengio S (2017) Learning to remember rare events. http:\/\/arxiv.org\/abs\/1703.03129"},{"key":"8175_CR19","unstructured":"Kipf T.N, Welling M (2017) Semi-supervised classification with graph convolutional networks. In Proceedings of the 6th International Conference on Learning Representations"},{"key":"8175_CR20","first-page":"1097","volume":"25","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 25:1097\u20131105","journal-title":"Adv Neural Inf Process Syst"},{"key":"8175_CR21","doi-asserted-by":"crossref","unstructured":"Lee K, Maji S, Ravichandran A, Soatto S (2019) Meta-learning with differentiable convex optimization. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10657\u201310665","DOI":"10.1109\/CVPR.2019.01091"},{"key":"8175_CR22","doi-asserted-by":"crossref","unstructured":"Li J, Cheng K, Wu L, Liu H (2018) Streaming link prediction on dynamic attributed networks. In: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, pp. 369\u2013377","DOI":"10.1145\/3159652.3159674"},{"key":"8175_CR23","doi-asserted-by":"crossref","unstructured":"Li R, Wang S, Zhu F, Huang J (2018) Adaptive graph convolutional neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a032","DOI":"10.1609\/aaai.v32i1.11691"},{"key":"8175_CR24","unstructured":"Liu Y, Lee J, Park M, Kim S, Yang E, Hwang S.J, Yang Y (2019) Learning to propagate labels: Transductive propagation network for few-shot learning. In: In Proceedings of the7th International Conference on Learning Representation"},{"key":"8175_CR25","doi-asserted-by":"crossref","unstructured":"McAuley J, Pandey R, Leskovec J (2015) Inferring networks of substitutable and complementary products. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785\u2013794","DOI":"10.1145\/2783258.2783381"},{"key":"8175_CR26","unstructured":"Mishra N, Rohaninejad M, Chen X, Abbeel P (2018) A simple neural attentive meta-learner. In: In Proceedings of the 6th International Conference on Learning Representation"},{"key":"8175_CR27","unstructured":"Nichol A, Achiam J, Schulman J (2018) On first-order meta-learning algorithms. http:\/\/arxiv.org\/abs\/1803.02999"},{"key":"8175_CR28","doi-asserted-by":"crossref","unstructured":"Perozzi B, Al-Rfou R, Skiena S (2014) Deepwalk: Online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 701\u2013710","DOI":"10.1145\/2623330.2623732"},{"key":"8175_CR29","doi-asserted-by":"crossref","unstructured":"Pfeiffer\u00a0III J.J, Moreno S, La\u00a0Fond T, Neville J, Gallagher B (2014) Attributed graph models: Modeling network structure with correlated attributes. In: Proceedings of the 23rd International Conference on World wide web, pp. 831\u2013842","DOI":"10.1145\/2566486.2567993"},{"key":"8175_CR30","unstructured":"Ravi S, Larochelle H (2017) Optimization as a model for few-shot learning. In: In Proceedings of the 5th International Conference on Learning Representation"},{"key":"8175_CR31","unstructured":"Ren M, Triantafillou E, Ravi S, Snell J, Swersky K, Tenenbaum J.B, Larochelle H, Zemel R.S (2018) Meta-learning for semi-supervised few-shot classification. In: In Proceedings of the 6th International Conference on Learning Representation"},{"key":"8175_CR32","doi-asserted-by":"crossref","unstructured":"Rios A, Kavuluru R (2018) Few-shot and zero-shot multi-label learning for structured label spaces. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing, vol. 2018, p. 3132. NIH Public Access","DOI":"10.18653\/v1\/D18-1352"},{"key":"8175_CR33","unstructured":"Santoro A, Bartunov S, Botvinick M, Wierstra D, Lillicrap T (2016) Meta-learning with memory-augmented neural networks. In: International Conference on Machine Learning, pp. 1842\u20131850. PMLR"},{"issue":"1","key":"8175_CR34","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, Hagenbuchner M, Monfardini G (2009) The graph neural network model. IEEE Trans Neural Netw 20(1):61","journal-title":"IEEE Trans Neural Netw"},{"issue":"3","key":"8175_CR35","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1609\/aimag.v29i3.2157","volume":"29","author":"P Sen","year":"2008","unstructured":"Sen P, Namata G, Bilgic M, Getoor L, Galligher B, Eliassi-Rad T (2008) Collective classification in network data. AI Magazine 29(3):93\u201393","journal-title":"AI Magazine"},{"key":"8175_CR36","unstructured":"Snell J, Swersky K, Zemel R.S (2017) Prototypical networks for few-shot learning. http:\/\/arxiv.org\/abs\/1703.05175"},{"key":"8175_CR37","doi-asserted-by":"crossref","unstructured":"Sung F, Yang Y, Zhang L, Xiang T, Torr P.H, Hospedales T.M (2018) Learning to compare: Relation network for few-shot learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1199\u20131208","DOI":"10.1109\/CVPR.2018.00131"},{"key":"8175_CR38","doi-asserted-by":"crossref","unstructured":"Tang J, Qu M, Wang M, Zhang M, Yan J, Mei Q (2015) Line: Large-scale information network embedding. In: Proceedings of the 24th International Conference on World Wide Web, pp. 1067\u20131077","DOI":"10.1145\/2736277.2741093"},{"key":"8175_CR39","doi-asserted-by":"crossref","unstructured":"Tang J, Zhang J, Yao L, Li J, Zhang L, Su Z (2008) Arnetminer: extraction and mining of academic social networks. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 990\u2013998","DOI":"10.1145\/1401890.1402008"},{"key":"8175_CR40","unstructured":"Velickovic P, Cucurull G, Casanova A, Romero A, Li\u00f2 P, Bengio Y (2017) Graph attention networks.http:\/\/arxiv.org\/abs\/1710.10903"},{"key":"8175_CR41","first-page":"3630","volume":"29","author":"O Vinyals","year":"2016","unstructured":"Vinyals O, Blundell C, Lillicrap T, Wierstra D et al (2016) Matching networks for one shot learning. Adv Neural Inf Process Syst 29:3630\u20133638","journal-title":"Adv Neural Inf Process Syst"},{"key":"8175_CR42","doi-asserted-by":"crossref","unstructured":"Wang J, Ding K, Hong L, Liu H, Caverlee J (2020) Next-item recommendation with sequential hypergraphs. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1101\u20131110","DOI":"10.1145\/3397271.3401133"},{"key":"8175_CR43","doi-asserted-by":"crossref","unstructured":"Wang N, Luo M, Ding K, Zhang L, Li J, Zheng Q(2020) Graph few-shot learning with attribute matching. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp. 1545\u20131554","DOI":"10.1145\/3340531.3411923"},{"key":"8175_CR44","doi-asserted-by":"crossref","unstructured":"Wang X, Cui P, Wang J, Pei J, Zhu W, Yang S (2017) Community preserving network embedding. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a031, pp. 203\u2013209","DOI":"10.1609\/aaai.v31i1.10488"},{"key":"8175_CR45","unstructured":"Wu F, Souza A, Zhang T, Fifty C, Yu T, Weinberger K (2019) Simplifying graph convolutional networks. In: International Conference on Machine Learning, pp. 6861\u20136871. PMLR"},{"key":"8175_CR46","doi-asserted-by":"crossref","unstructured":"Zhang J, Zhao C, Ni B, Xu M, Yang X (2019) Variational few-shot learning. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1685\u20131694","DOI":"10.1109\/ICCV.2019.00177"},{"issue":"5","key":"8175_CR47","doi-asserted-by":"publisher","first-page":"1426","DOI":"10.1162\/neco_a_01071","volume":"30","author":"L Zhang","year":"2018","unstructured":"Zhang L, Liu J, Luo M, Chang X, Zheng Q (2018) Deep semisupervised zero-shot learning with maximum mean discrepancy. Neural Comput 30(5):1426\u20131447","journal-title":"Neural Comput"},{"key":"8175_CR48","doi-asserted-by":"crossref","unstructured":"Zhang S, Tong H (2016) Final: Fast attributed network alignment. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1345\u20131354","DOI":"10.1145\/2939672.2939766"},{"key":"8175_CR49","doi-asserted-by":"crossref","unstructured":"Zhou F, Cao C, Zhang K, Trajcevski G, Zhong T, Geng J (2019) Meta-gnn: On few-shot node classification in graph meta-learning. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp. 2357\u20132360","DOI":"10.1145\/3357384.3358106"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-08175-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-022-08175-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-08175-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,20]],"date-time":"2023-01-20T21:42:23Z","timestamp":1674250943000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-022-08175-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,20]]},"references-count":49,"alternative-id":["8175"],"URL":"https:\/\/doi.org\/10.1007\/s00521-022-08175-4","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2023,1,20]]},"assertion":[{"value":"19 June 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 December 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 January 2023","order":3,"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"}}]}}