{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,30]],"date-time":"2026-05-30T08:00:56Z","timestamp":1780128056897,"version":"3.54.0"},"reference-count":65,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T00:00:00Z","timestamp":1774828800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T00:00:00Z","timestamp":1774828800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Outstanding Innovative Talents Cultivation Funded Programs for Graduate Students of Jinan University","award":["2025CXY336, 2025CXY339, 2025CXY402"],"award-info":[{"award-number":["2025CXY336, 2025CXY339, 2025CXY402"]}]},{"name":"Special Funds for the Cultivation of Guangdong College Students\u2019 Scientific and Technological Innovation","award":["pdjh2025ak028"],"award-info":[{"award-number":["pdjh2025ak028"]}]},{"name":"Cybersecurity College Student Innovation Funding Program"},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62272198"],"award-info":[{"award-number":["62272198"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100021171","name":"Guangdong Basic and Applied Basic Research Foundation","doi-asserted-by":"crossref","award":["2024A1515010121"],"award-info":[{"award-number":["2024A1515010121"]}],"id":[{"id":"10.13039\/501100021171","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. &amp; Cyber."],"published-print":{"date-parts":[[2026,5]]},"DOI":"10.1007\/s13042-026-03060-1","type":"journal-article","created":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T08:03:58Z","timestamp":1774857838000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["EdgeGFL: rethinking edge information in graph feature preference learning"],"prefix":"10.1007","volume":"17","author":[{"given":"Shengda","family":"Zhuo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiwang","family":"Fang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongguang","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ning","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuewei","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siheng","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yin","family":"Tang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Min","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuqiang","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,3,30]]},"reference":[{"key":"3060_CR1","doi-asserted-by":"publisher","first-page":"119658","DOI":"10.1016\/j.eswa.2023.119658","volume":"219","author":"Y Wang","year":"2023","unstructured":"Wang Y, Wang C, Zhan J, Ma W, Jiang Y (2023) Text fcg: Fusing contextual information via graph learning for text classification. Expert Syst Appl 219:119658","journal-title":"Expert Syst Appl"},{"issue":"5","key":"3060_CR2","first-page":"1","volume":"16","author":"X Wang","year":"2025","unstructured":"Wang X, Wu L, Hong L, Liu H, Fu Y (2025) Llm-enhanced user-item interactions: leveraging edge information for optimized recommendations. ACM Trans Intell Syst Technol 16(5):1\u201324","journal-title":"ACM Trans Intell Syst Technol"},{"key":"3060_CR3","unstructured":"CAOS L, XU QG (2015) Learning graph representations with global structural information. Proceedings of CIKM"},{"key":"3060_CR4","doi-asserted-by":"crossref","unstructured":"Du L, Gao F, Chen X, Jia R, Wang J, Zhang J, Han S, Zhang D (2021) Tabularnet: A neural network architecture for understanding semantic structures of tabular data. In: Proceedings of KDD, pp. 322\u2013331","DOI":"10.1145\/3447548.3467228"},{"key":"3060_CR5","doi-asserted-by":"publisher","first-page":"726","DOI":"10.1016\/j.ins.2019.10.015","volume":"512","author":"Z Yu","year":"2020","unstructured":"Yu Z, Zhang Z, Chen H, Shao J (2020) Structured subspace embedding on attributed networks. Inf Sci 512:726\u2013740","journal-title":"Inf Sci"},{"key":"3060_CR6","doi-asserted-by":"crossref","unstructured":"Zhuo S, Wang T, Li L, Zhou Z, Guan Z, Tang Y, Chen M, Huang S (2025) Redefining edge representations for enhanced information propagation on gnns. J Intell Inform Syst, 1\u201325","DOI":"10.1007\/s10844-025-00993-x"},{"key":"3060_CR7","doi-asserted-by":"crossref","unstructured":"Wang D, Cui P, Zhu W (2016) Structural deep network embedding. In: Proceedings of KDD, pp. 1225\u20131234","DOI":"10.1145\/2939672.2939753"},{"key":"3060_CR8","doi-asserted-by":"crossref","unstructured":"Cao S, Lu W, Xu Q (2016) Deep neural networks for learning graph representations. In: Proceedings of AAAI, vol. 30","DOI":"10.1609\/aaai.v30i1.10179"},{"issue":"2","key":"3060_CR9","first-page":"431","volume":"33","author":"Z He","year":"2019","unstructured":"He Z, Liu J, Zeng Y, Wei L, Huang Y (2019) Content to node: self-translation network embedding. IEEE Trans Knowl Data Eng 33(2):431\u2013443","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"3060_CR10","doi-asserted-by":"publisher","first-page":"104129","DOI":"10.1016\/j.artint.2024.104129","volume":"331","author":"T He","year":"2024","unstructured":"He T, Liu Y, Ong Y-S, Wu X, Luo X (2024) Polarized message-passing in graph neural networks. Artif Intell 331:104129","journal-title":"Artif Intell"},{"key":"3060_CR11","unstructured":"Veli\u010dkovi\u0107 P, Cucurull G, Casanova A, Romero A, Lio P, Bengio Y (2017) Graph attention networks. arXiv preprint arXiv:1710.10903"},{"key":"3060_CR12","doi-asserted-by":"publisher","first-page":"102335","DOI":"10.1016\/j.is.2023.102335","volume":"121","author":"B Wu","year":"2024","unstructured":"Wu B, Chao K-M, Li Y (2024) Heterogeneous graph neural networks for fraud detection and explanation in supply chain finance. Inf Syst 121:102335","journal-title":"Inf Syst"},{"key":"3060_CR13","unstructured":"Lipton ZC, Berkowitz J, Elkan C (2015) A critical review of recurrent neural networks for sequence learning. arXiv preprint arXiv:1506.00019"},{"key":"3060_CR14","doi-asserted-by":"crossref","unstructured":"Shi C (2022) Heterogeneous graph neural networks. Graph Neural Networks: Foundations, Frontiers, and Applications, 351\u2013369","DOI":"10.1007\/978-981-16-6054-2_16"},{"key":"3060_CR15","doi-asserted-by":"crossref","unstructured":"Wang X, Ji H, Shi C, Wang B, Ye Y, Cui P, Yu PS (2019) Heterogeneous graph attention network. In: The World Wide Web Conference, pp. 2022\u20132032","DOI":"10.1145\/3308558.3313562"},{"key":"3060_CR16","doi-asserted-by":"publisher","first-page":"111355","DOI":"10.1016\/j.knosys.2023.111355","volume":"285","author":"M Zhao","year":"2024","unstructured":"Zhao M, Jia AL (2024) Dahgn: degree-aware heterogeneous graph neural network. Knowl-Based Syst 285:111355","journal-title":"Knowl-Based Syst"},{"key":"3060_CR17","doi-asserted-by":"crossref","unstructured":"Xu L, He ZY, Wang K, Wang CD, Huang SQ (2022) Explicit message-passing heterogeneous graph neural network. IEEE Transactions on Knowledge and Data Engineering","DOI":"10.1109\/TKDE.2022.3185128"},{"key":"3060_CR18","doi-asserted-by":"crossref","unstructured":"Ding Y, Yao Q, Zhao H, Zhang T (2021) Diffmg: differentiable meta graph search for heterogeneous graph neural networks. In: Proceedings of KDD, pp. 279\u2013288","DOI":"10.1145\/3447548.3467447"},{"key":"3060_CR19","doi-asserted-by":"publisher","first-page":"111618","DOI":"10.1016\/j.knosys.2024.111618","volume":"292","author":"Y Li","year":"2024","unstructured":"Li Y, Jian C, Zang G, Song C, Yuan X (2024) Node classification oriented adaptive multichannel heterogeneous graph neural network. Knowl-Based Syst 292:111618","journal-title":"Knowl-Based Syst"},{"key":"3060_CR20","unstructured":"Kipf TN, Welling M (2016) Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907"},{"key":"3060_CR21","doi-asserted-by":"crossref","unstructured":"Yu P, Fu C, Yu Y, Huang C, Zhao Z, Dong J (2022) Multiplex heterogeneous graph convolutional network. In: Proceedings of KDD, pp. 2377\u20132387","DOI":"10.1145\/3534678.3539482"},{"issue":"6","key":"3060_CR22","first-page":"1","volume":"3","author":"E Million","year":"2007","unstructured":"Million E (2007) The hadamard product Course. Notes 3(6):1\u20137","journal-title":"Notes"},{"key":"3060_CR23","unstructured":"Goldberg Y, Levy O (2014) word2vec explained: deriving mikolov et al.\u2019s negative-sampling word-embedding method. arXiv preprint arXiv:1402.3722"},{"issue":"5500","key":"3060_CR24","doi-asserted-by":"publisher","first-page":"2323","DOI":"10.1126\/science.290.5500.2323","volume":"290","author":"ST Roweis","year":"2000","unstructured":"Roweis ST, Saul LK (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500):2323\u20132326","journal-title":"Science"},{"issue":"5500","key":"3060_CR25","doi-asserted-by":"publisher","first-page":"2319","DOI":"10.1126\/science.290.5500.2319","volume":"290","author":"JB Tenenbaum","year":"2000","unstructured":"Tenenbaum JB, Silva V, Langford JC (2000) A global geometric framework for nonlinear dimensionality reduction. Science 290(5500):2319\u20132323","journal-title":"Science"},{"key":"3060_CR26","doi-asserted-by":"crossref","unstructured":"Belkin M, Niyogi P (2001) Laplacian eigenmaps and spectral techniques for embedding and clustering. Proceedings of NeurIPS 14","DOI":"10.7551\/mitpress\/1120.003.0080"},{"key":"3060_CR27","unstructured":"Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J (2013) Distributed representations of words and phrases and their compositionality. Proceedings of NeurIPS 26"},{"key":"3060_CR28","doi-asserted-by":"crossref","unstructured":"Perozzi B, Al-Rfou R, Skiena S (2014) Deepwalk: Online learning of social representations. In: Proceedings of KDD, pp. 701\u2013710","DOI":"10.1145\/2623330.2623732"},{"key":"3060_CR29","doi-asserted-by":"publisher","first-page":"1318","DOI":"10.1109\/TASLP.2021.3065201","volume":"29","author":"Y Wang","year":"2021","unstructured":"Wang Y, Cui L, Zhang Y (2021) Improving skip-gram embeddings using bert. IEEE\/ACM Trans Audio Speech Lang Process 29:1318\u20131328","journal-title":"IEEE\/ACM Trans Audio Speech Lang Process"},{"key":"3060_CR30","doi-asserted-by":"crossref","unstructured":"Grover A, Leskovec J (2016) node2vec: scalable feature learning for networks. In: Proceedings of KDD, pp. 855\u2013864","DOI":"10.1145\/2939672.2939754"},{"key":"3060_CR31","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 AAAI, vol. 31","DOI":"10.1609\/aaai.v31i1.10488"},{"issue":"4","key":"3060_CR32","first-page":"1","volume":"15","author":"J-H Li","year":"2021","unstructured":"Li J-H, Huang L, Wang C-D, Huang D, Lai J-H, Chen P (2021) Attributed network embedding with micro-meso structure. ACM Trans Knowl Discov Data 15(4):1\u201326","journal-title":"ACM Trans Knowl Discov Data"},{"key":"3060_CR33","doi-asserted-by":"publisher","first-page":"106207","DOI":"10.1016\/j.neunet.2024.106207","volume":"173","author":"W Ju","year":"2024","unstructured":"Ju W, Fang Z, Gu Y, Liu Z, Long Q, Qiao Z, Qin Y, Shen J, Sun F, Xiao Z et al (2024) A comprehensive survey on deep graph representation learning. Neural Netw 173:106207","journal-title":"Neural Netw"},{"key":"3060_CR34","doi-asserted-by":"publisher","first-page":"510","DOI":"10.3758\/BF03193020","volume":"39","author":"JA Bullinaria","year":"2007","unstructured":"Bullinaria JA, Levy JP (2007) Extracting semantic representations from word co-occurrence statistics: a computational study. Behav Res Methods 39:510\u2013526","journal-title":"Behav Res Methods"},{"key":"3060_CR35","doi-asserted-by":"crossref","unstructured":"Tu C, Liu H, Liu Z, Sun M (2017) Cane: context-aware network embedding for relation modeling. In: Proceedings of ACL, pp. 1722\u20131731","DOI":"10.18653\/v1\/P17-1158"},{"key":"3060_CR36","doi-asserted-by":"crossref","unstructured":"Liu J, He Z, Wei L, Huang Y (2018) Content to node: self-translation network embedding. In: Proceedings of KDD, pp. 1794\u20131802","DOI":"10.1145\/3219819.3219988"},{"key":"3060_CR37","doi-asserted-by":"crossref","unstructured":"Gao H, Huang H (2018) Deep attributed network embedding. In: Proceedings of IJCAI","DOI":"10.24963\/ijcai.2018\/467"},{"key":"3060_CR38","doi-asserted-by":"crossref","unstructured":"Gao H, Huang H (2018) Self-paced network embedding. In: Proceedings of KDD, pp. 1406\u20131415","DOI":"10.1145\/3219819.3220041"},{"issue":"4","key":"3060_CR39","doi-asserted-by":"publisher","first-page":"1556","DOI":"10.1109\/TCYB.2018.2871503","volume":"50","author":"X Shen","year":"2018","unstructured":"Shen X, Chung FL (2018) Deep network embedding for graph representation learning in signed networks. IEEE Trans Cybern 50(4):1556\u20131568","journal-title":"IEEE Trans Cybern"},{"issue":"7","key":"3060_CR40","doi-asserted-by":"publisher","first-page":"5908","DOI":"10.1109\/TCYB.2020.3035066","volume":"52","author":"C-D Wang","year":"2020","unstructured":"Wang C-D, Shi W, Huang L, Lin K-Y, Huang D, Philip SY (2020) Node pair information preserving network embedding based on adversarial networks. IEEE Trans Cybern 52(7):5908\u20135922","journal-title":"IEEE Trans Cybern"},{"key":"3060_CR41","doi-asserted-by":"crossref","unstructured":"Hu F, Zhu Y, Wu S, Wang L, Tan T (2019) Hierarchical graph convolutional networks for semi-supervised node classification. arXiv preprint arXiv:1902.06667","DOI":"10.24963\/ijcai.2019\/630"},{"key":"3060_CR42","doi-asserted-by":"crossref","unstructured":"Xu F, Lian J, Han Z, Li Y, Xu Y, Xie X (2019) Relation-aware graph convolutional networks for agent-initiated social e-commerce recommendation. In: Proceedings of CIKM, pp. 529\u2013538","DOI":"10.1145\/3357384.3357924"},{"key":"3060_CR43","doi-asserted-by":"crossref","unstructured":"Jiang J, Wei Y, Feng Y, Cao J, Gao Y (2019) Dynamic hypergraph neural networks. In: Proceedings of IJCAI, pp. 2635\u20132641","DOI":"10.24963\/ijcai.2019\/366"},{"key":"3060_CR44","unstructured":"Hamilton W, Ying Z, Leskovec J (2017) Inductive representation learning on large graphs. Proceedings of NeurIPS 30"},{"key":"3060_CR45","doi-asserted-by":"crossref","unstructured":"Pan S Hu R, Fung Sf, Long G, Jiang J, Zhang C (2019) Learning graph embedding with adversarial training methods. IEEE Trans Cybern 50(6), 2475\u20132487","DOI":"10.1109\/TCYB.2019.2932096"},{"key":"3060_CR46","doi-asserted-by":"crossref","unstructured":"Jiang X, Ji P, Li S (2019) Censnet: convolution with edge-node switching in graph neural networks. In: Proceedings of IJCAI, pp. 2656\u20132662","DOI":"10.24963\/ijcai.2019\/369"},{"key":"3060_CR47","doi-asserted-by":"crossref","unstructured":"Ye R, Li X, Fang Y, Zang H, Wang M (2019) A vectorized relational graph convolutional network for multi-relational network alignment. In: Proceedings of IJCAI, pp. 4135\u20134141","DOI":"10.24963\/ijcai.2019\/574"},{"key":"3060_CR48","unstructured":"Jeon HJ, Lee OJ, et al. (2025) Mitigating degree bias in graph representation learning with learnable structural augmentation and structural self-attention. IEEE Trans Netw Sci Eng"},{"key":"3060_CR49","doi-asserted-by":"crossref","unstructured":"Erfani SH, Fadaeieslam MJ, Mortazavi R, Rahmanimanesh M (2025) Ssgnn: Simple siamese graph neural networks for out-of-distribution generalization. Knowledge-Based Syst, 114290","DOI":"10.1016\/j.knosys.2025.114290"},{"key":"3060_CR50","doi-asserted-by":"crossref","unstructured":"Lee OJ, et al. (2025) Pre-training graph neural networks on molecules by using subgraph-conditioned graph information bottleneck. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 39, pp. 17204\u201317213","DOI":"10.1609\/aaai.v39i16.33891"},{"key":"3060_CR51","doi-asserted-by":"crossref","unstructured":"Lee OJ, et al. (2024) Transitivity-preserving graph representation learning for bridging local connectivity and role-based similarity. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38, pp. 12456\u201312465","DOI":"10.1609\/aaai.v38i11.29138"},{"issue":"8","key":"3060_CR52","doi-asserted-by":"publisher","first-page":"4168","DOI":"10.3390\/s23084168","volume":"23","author":"VT Hoang","year":"2023","unstructured":"Hoang VT, Jeon HJ, You ES, Yoon Y, Jung S, Lee OJ (2023) Graph representation learning and its applications: a survey. Sensors 23(8):4168","journal-title":"Sensors"},{"key":"3060_CR53","doi-asserted-by":"crossref","unstructured":"He R, Ravula A, Kanagal B, Ainslie J (2020) Realformer: Transformer likes residual attention. arXiv preprint arXiv:2012.11747","DOI":"10.18653\/v1\/2021.findings-acl.81"},{"key":"3060_CR54","doi-asserted-by":"crossref","unstructured":"Bollacker K, Evans C, Paritosh P, Sturge T, Taylor J (2008) Freebase: a collaboratively created graph database for structuring human knowledge. In: Proceedings of SIGMOD, pp. 1247\u20131250","DOI":"10.1145\/1376616.1376746"},{"issue":"10","key":"3060_CR55","doi-asserted-by":"publisher","first-page":"4854","DOI":"10.1109\/TKDE.2020.3045924","volume":"34","author":"C Yang","year":"2020","unstructured":"Yang C, Xiao Y, Zhang Y, Sun Y, Han J (2020) Heterogeneous network representation learning: a unified framework with survey and benchmark. IEEE Trans Knowl Data Eng 34(10):4854\u20134873","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"3060_CR56","unstructured":"Yun S, Jeong M, Kim R, Kang J, Kim HJ (2019) Graph transformer networks. Proceedings of NeurIPS 32"},{"key":"3060_CR57","doi-asserted-by":"crossref","unstructured":"Hu Z, Dong Y, Wang K, Sun Y (2020) Heterogeneous graph transformer. In: Proceedings of WWW, pp. 2704\u20132710","DOI":"10.1145\/3366423.3380027"},{"key":"3060_CR58","doi-asserted-by":"crossref","unstructured":"Schlichtkrull M, Kipf TN, Bloem P, Van Den\u00a0Berg R, Titov I, Welling M (2018) Modeling relational data with graph convolutional networks. In: Proceedings of ESWC, pp. 593\u2013607 . Springer","DOI":"10.1007\/978-3-319-93417-4_38"},{"key":"3060_CR59","doi-asserted-by":"crossref","unstructured":"Fu X, Zhang J, Meng Z, King I (2020) Magnn: Metapath aggregated graph neural network for heterogeneous graph embedding. In: Proceedings of WWW, pp. 2331\u20132341","DOI":"10.1145\/3366423.3380297"},{"key":"3060_CR60","doi-asserted-by":"crossref","unstructured":"Lv Q, Ding M, Liu Q, Chen Y, Feng W, He S, Zhou C, Jiang J, Dong Y, Tang J (2021) Are we really making much progress? revisiting, benchmarking and refining heterogeneous graph neural networks. In: Proceedings of KDD, pp. 1150\u20131160","DOI":"10.1145\/3447548.3467350"},{"key":"3060_CR61","doi-asserted-by":"crossref","unstructured":"Yang X, Yan M, Pan S, Ye X, Fan D (2023) Simple and efficient heterogeneous graph neural network. In: Proceedings of AAAI, 37: 10816\u201310824","DOI":"10.1609\/aaai.v37i9.26283"},{"key":"3060_CR62","doi-asserted-by":"crossref","unstructured":"Li Z, Ren Q, Chen L, Sui X, Li J (2022) Multi-hierarchical spatial-temporal graph convolutional networks for traffic flow forecasting. In: Proceedings of ICPR, pp. 4913\u20134919 . IEEE","DOI":"10.1109\/ICPR56361.2022.9956477"},{"key":"3060_CR63","doi-asserted-by":"publisher","first-page":"266","DOI":"10.1016\/j.neunet.2023.11.030","volume":"170","author":"X Fu","year":"2024","unstructured":"Fu X, King I (2024) Mecch: metapath context convolution-based heterogeneous graph neural networks. Neural Netw 170:266\u2013275","journal-title":"Neural Netw"},{"key":"3060_CR64","doi-asserted-by":"publisher","first-page":"119982","DOI":"10.1016\/j.eswa.2023.119982","volume":"224","author":"Z Wang","year":"2023","unstructured":"Wang Z, Yu D, Li Q, Shen S, Yao S (2023) Sr-hgn: semantic-and relation-aware heterogeneous graph neural network. Expert Syst Appl 224:119982","journal-title":"Expert Syst Appl"},{"issue":"12","key":"3060_CR65","doi-asserted-by":"publisher","first-page":"2257","DOI":"10.1109\/TKDE.2018.2819980","volume":"30","author":"L Liao","year":"2018","unstructured":"Liao L, He X, Zhang H, Chua TS (2018) Attributed social network embedding. IEEE Trans Knowl Data Eng 30(12):2257\u20132270","journal-title":"IEEE Trans Knowl Data Eng"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-026-03060-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-026-03060-1","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-026-03060-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,30]],"date-time":"2026-05-30T07:02:16Z","timestamp":1780124536000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-026-03060-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,30]]},"references-count":65,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2026,5]]}},"alternative-id":["3060"],"URL":"https:\/\/doi.org\/10.1007\/s13042-026-03060-1","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,30]]},"assertion":[{"value":"17 May 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 March 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 March 2026","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 no Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"240"}}