{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T08:53:28Z","timestamp":1778662408720,"version":"3.51.4"},"reference-count":54,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2022,7,11]],"date-time":"2022-07-11T00:00:00Z","timestamp":1657497600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,7,11]],"date-time":"2022-07-11T00:00:00Z","timestamp":1657497600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61773020"],"award-info":[{"award-number":["61773020"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Scientometrics"],"published-print":{"date-parts":[[2022,8]]},"DOI":"10.1007\/s11192-022-04419-1","type":"journal-article","created":{"date-parts":[[2022,7,12]],"date-time":"2022-07-12T21:12:03Z","timestamp":1657660323000},"page":"4847-4872","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["A multi-view method of scientific paper classification via heterogeneous graph embeddings"],"prefix":"10.1007","volume":"127","author":[{"given":"Yiqin","family":"Lv","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0391-8725","authenticated-orcid":false,"given":"Zheng","family":"Xie","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaojing","family":"Zuo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yiping","family":"Song","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,7,11]]},"reference":[{"issue":"7","key":"4419_CR1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0158423","volume":"11","author":"T Achakulvisut","year":"2016","unstructured":"Achakulvisut, T., Acuna, D. E., Ruangrong, T., & Kording, K. (2016). Science concierge: A fast content based recommendation system for scientific publications. PLoS ONE, 11(7), e0158423.","journal-title":"PLoS ONE"},{"key":"4419_CR2","doi-asserted-by":"crossref","unstructured":"Alsmadi, K. M., Omar, K., Noah, A. S., & Almarashdah, I. (2009). Performance comparison of multi-layer perceptron (back propagation, delta rule and perceptron) algorithms in Neural Networks. IEEE international advance computing conference (pp. 296\u2013299).","DOI":"10.1109\/IADCC.2009.4809024"},{"key":"4419_CR3","unstructured":"Arman, C., Sergey, F., Beltagy, I., Doug, D., & Daniel, W. (2020). Specter: Document-level representation learning using citation-informed transformers. ACL (pp. 2270\u20132282)."},{"key":"4419_CR4","unstructured":"Ashish, V., Noam, S., Niki, P., Jakob, U., Llion, J., Aidan, N. G., \u0141ukasz, K., & Illia, P. (2017). Attention is all you need. NeurIPS (pp. 6000\u20136010)."},{"key":"4419_CR5","doi-asserted-by":"crossref","unstructured":"Beltagy, I., Kyle, L., & Arman, C. (2019). Scibert: A pretrained language model for scientific text. EMNLP (pp. 3615\u20133620).","DOI":"10.18653\/v1\/D19-1371"},{"issue":"2","key":"4419_CR6","first-page":"123","volume":"24","author":"L Breiman","year":"1996","unstructured":"Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123\u2013140.","journal-title":"Machine Learning"},{"issue":"1","key":"4419_CR7","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5\u201332.","journal-title":"Machine Learning"},{"key":"4419_CR8","first-page":"151","volume-title":"Classification and regression trees","author":"L Breiman","year":"1984","unstructured":"Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984). Classification and regression trees (Vol. 432, pp. 151\u2013166). Belmont, CA: International Group, Wadsworth."},{"key":"4419_CR9","doi-asserted-by":"crossref","unstructured":"Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. SIGKDD (pp. 785\u2013794).","DOI":"10.1145\/2939672.2939785"},{"key":"4419_CR10","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1109\/TIT.1967.1053964","volume":"13","author":"T Cover","year":"1967","unstructured":"Cover, T., & Hart, P. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13, 21\u201327.","journal-title":"IEEE Transactions on Information Theory"},{"key":"4419_CR11","first-page":"993","volume":"3","author":"BM David","year":"2003","unstructured":"David, B. M., Andrew, N. Y., & Michael, J. I. (2003). Latent Dirichlet Allocatio. Journal of Machine Learning Research, 3, 993\u20131102.","journal-title":"Journal of Machine Learning Research"},{"key":"4419_CR12","unstructured":"Devlin, J., Chang, M., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. NAACL (pp. 4171\u20134186)."},{"key":"4419_CR13","doi-asserted-by":"crossref","unstructured":"Ding, K., Wang, J., Li, J., Li, D., & Liu, H. (2020). Be more with less: Hypergraph attention networks for inductive text classification. EMNLP (pp. 4927\u20134936).","DOI":"10.18653\/v1\/2020.emnlp-main.399"},{"key":"4419_CR14","doi-asserted-by":"crossref","unstructured":"Ech-Chouyyekh, M., Omara, H., & Lazaar, M. (2019). Scientific paper classification using convolutional neural networks. Proceedings of the 4th international conference on big data and internet of things (pp. 1\u20136).","DOI":"10.1145\/3372938.3372951"},{"issue":"1","key":"4419_CR15","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1006\/jcss.1997.1504","volume":"55","author":"Y Freund","year":"1997","unstructured":"Freund, Y., & Robert, E. S. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119\u2013139.","journal-title":"Journal of Computer and System Sciences"},{"key":"4419_CR16","doi-asserted-by":"publisher","first-page":"383","DOI":"10.1007\/978-3-319-56608-5_30","volume-title":"Advances in Information Retrieval","author":"S Ganguly","year":"2017","unstructured":"Ganguly, S., & Pudi, V. (2017). Paper2vec: Combining graph and text information for scientific paper representation. Advances in Information Retrieval (pp. 383\u2013395). Berlin: Springer."},{"key":"4419_CR17","doi-asserted-by":"crossref","unstructured":"Gao, M., Chen, L., He, X., & Zhou, A. (2018). Bine: Bipartite network embedding. SIGIR (pp. 715\u2013724).","DOI":"10.1145\/3209978.3209987"},{"key":"4419_CR18","unstructured":"Grave, E., Mikolov, T., Joulin, A., & Bojanowski, P. (2017). Bag of tricks for efficient text classification. EACL (pp. 427\u2013431)."},{"key":"4419_CR19","first-page":"53","volume":"13","author":"E Han","year":"2001","unstructured":"Han, E., Karypis, G., & Kumar, V. (2001). Text categorization using weight adjusted k-nearest neighbor classification. PAKDD, 13, 53\u201365.","journal-title":"PAKDD"},{"key":"4419_CR20","doi-asserted-by":"crossref","unstructured":"Jacovi, A., Shalom, O., & Goldberg, Y. (2018). Understanding convolutional neural networks for text classification. EMNLP (pp. 56\u201365).","DOI":"10.18653\/v1\/W18-5408"},{"issue":"3","key":"4419_CR21","doi-asserted-by":"publisher","first-page":"599","DOI":"10.1111\/coin.12225","volume":"35","author":"R Jin","year":"2019","unstructured":"Jin, R., Lu, L., Lee, J., & Usman, A. (2019). Multi-representational convolutional neural networks for text classification. Computational Intelligence, 35(3), 599\u2013609.","journal-title":"Computational Intelligence"},{"key":"4419_CR22","doi-asserted-by":"crossref","unstructured":"Joachims, T. (1998). Text categorization with Support Vector Machines: Learning with many relevant feature. Machine Learning: ECML-98 (pp. 137\u2013142).","DOI":"10.1007\/BFb0026683"},{"key":"4419_CR23","doi-asserted-by":"publisher","first-page":"493","DOI":"10.1108\/00220410410560573","volume":"60","author":"KS Jones","year":"2004","unstructured":"Jones, K. S. (2004). A statistical interpretation of term specificity and its application in retrieval. Journal of Documentation, 60, 493\u2013502.","journal-title":"Journal of Documentation"},{"key":"4419_CR24","unstructured":"Kipf, N. T., & Welling, M. (2017). Semi-supervised classification with graph convolutional networks. ICLR."},{"key":"4419_CR25","doi-asserted-by":"publisher","first-page":"226","DOI":"10.1109\/TETC.2018.2830698","volume":"9","author":"X Kong","year":"2018","unstructured":"Kong, X., Mao, M., Wang, W., Liu, J., & Xu, B. (2018). VOPRec: Vector representation learning of papers with text information and structural identity for recommendation. IEEE Transactions on Emerging Topics in Computing, 9, 226\u2013237.","journal-title":"IEEE Transactions on Emerging Topics in Computing"},{"key":"4419_CR26","doi-asserted-by":"publisher","first-page":"5881","DOI":"10.1007\/s11192-021-03984-1","volume":"126","author":"D Kozlowski","year":"2021","unstructured":"Kozlowski, D., Dusdal, J., Pang, J., & Zilian, A. (2021). Semantic and relational spaces in science of science: Deep learning models for article vectorisation. Scientometrics, 126, 5881\u20135910.","journal-title":"Scientometrics"},{"key":"4419_CR27","unstructured":"Le, V. Q., & Mikolov, T. (2014). Distributed representations of sentences and documents. ICML (pp. 1188\u20131196)."},{"key":"4419_CR28","doi-asserted-by":"crossref","unstructured":"Li, X., Ding, D., Kao, B., Sun, Y., & Mamoulis, N. (2021). Leveraging meta-path contexts for classification in heterogeneous information networks. ICDE (pp. 912\u2013923).","DOI":"10.1109\/ICDE51399.2021.00084"},{"key":"4419_CR29","doi-asserted-by":"publisher","first-page":"6937","DOI":"10.1007\/s11192-021-04028-4","volume":"126","author":"Y Lu","year":"2021","unstructured":"Lu, Y., Luo, J., Xiao, Y., & Zhu, H. (2021). Text representation model of scientific papers based on fusing multi-viewpoint information and its quality assessment. Scientometrics, 126, 6937\u20136963.","journal-title":"Scientometrics"},{"issue":"3","key":"4419_CR30","doi-asserted-by":"publisher","first-page":"3401","DOI":"10.1016\/j.aej.2021.02.009","volume":"60","author":"X Luo","year":"2021","unstructured":"Luo, X. (2021). Efficient English text classification using selected Machine Learning Techniques. Alexandria Engineering Journal, 60(3), 3401\u20133409.","journal-title":"Alexandria Engineering Journal"},{"key":"4419_CR31","doi-asserted-by":"publisher","first-page":"404","DOI":"10.1145\/321075.321084","volume":"1","author":"EM Maron","year":"1961","unstructured":"Maron, E. M. (1961). Automatic indexing: An experimental inquiry. Journal of the ACM, 1, 404\u2013417.","journal-title":"Journal of the ACM"},{"key":"4419_CR32","doi-asserted-by":"publisher","DOI":"10.1111\/exsy.12613","volume":"38","author":"A Masmoudi","year":"2021","unstructured":"Masmoudi, A., Bellaaj, H., Drira, K., & Jmaiel, M. (2021). A co-training-based approach for the hierarchical multi-label classification of research papers. Expert Systems, 38, e12613.","journal-title":"Expert Systems"},{"key":"4419_CR33","first-page":"101","volume":"15","author":"DLT Mauro","year":"2021","unstructured":"Mauro, D. L. T., & Julio, C. (2021). SciKGraph: A knowledge graph approach to structure a scientific field. Journal of Informetrics, 15, 101\u2013109.","journal-title":"Journal of Informetrics"},{"key":"4419_CR34","unstructured":"Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013). Distributed representations of words and phrases and their compositionality. NeurIPS (pp. 3111\u20133119)."},{"key":"4419_CR35","doi-asserted-by":"crossref","unstructured":"Perozzi, B., Al-Rfou, R., & Skiena, S. (2014). Deepwalk: Online learning of social representations. SIGKDD (pp. 701\u2013710).","DOI":"10.1145\/2623330.2623732"},{"key":"4419_CR36","doi-asserted-by":"crossref","unstructured":"Quan, J., Li, Q., & Li, M. (2014). Computer science paper classification for CSAR. ICWL, pp. 34\u201343.","DOI":"10.1007\/978-3-319-13296-9_4"},{"issue":"1","key":"4419_CR37","first-page":"81","volume":"1","author":"JR Quinlan","year":"1986","unstructured":"Quinlan, J. R. (1986). Induction of decision trees. Machine Learning, 1(1), 81\u2013106.","journal-title":"Machine Learning"},{"key":"4419_CR38","volume-title":"C4.5: Programs for Machine Learning","author":"JR Quinlan","year":"1993","unstructured":"Quinlan, J. R. (1993). C4.5: Programs for Machine Learning. San Francisco: Morgan Kaufmann Publishers Inc."},{"key":"4419_CR39","doi-asserted-by":"publisher","first-page":"1124","DOI":"10.1016\/j.procs.2015.07.400","volume":"57","author":"B Ramesh","year":"2015","unstructured":"Ramesh, B., & Sathiaseelan, J. G. R. (2015). An advanced multi class instance selection based support vector machine for text classification. Procedia Computer Science, 57, 1124\u20131130.","journal-title":"Procedia Computer Science"},{"issue":"01","key":"4419_CR40","doi-asserted-by":"publisher","first-page":"2150004","DOI":"10.1142\/S0219649221500040","volume":"20","author":"AN Sajid","year":"2021","unstructured":"Sajid, A. N., Ahmad, M., Afzal, T. M., & Atta-ur-Rahman. (2021). Exploiting papers\u2019 reference\u2019s section for multi-label computer science research papers\u2019 classification. Journal of Information and Knowledge Management, 20(01), 2150004.","journal-title":"Journal of Information and Knowledge Management"},{"key":"4419_CR41","doi-asserted-by":"crossref","unstructured":"Sun, Y., Han, J., Yan, X., Yu, S. P. & Wu, T. (2011) PathSim: Meta path-based top-K similarity search in heterogeneous information networks. PVLDB, 992-1003.","DOI":"10.14778\/3402707.3402736"},{"issue":"3","key":"4419_CR42","doi-asserted-by":"publisher","first-page":"973","DOI":"10.1109\/TNNLS.2020.3036192","volume":"33","author":"Z Tan","year":"2022","unstructured":"Tan, Z., Chen, J., Kang, Q., Zhou, M., Abusorrah, A., & Sedraoui, K. (2022). Dynamic embedding projection-gated convolutional neural networks for text classification. IEEE Transactions on Neural Networks and Learning Systems, 33(3), 973\u2013982.","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"issue":"4","key":"4419_CR43","doi-asserted-by":"publisher","DOI":"10.1016\/j.joi.2020.101076","volume":"14","author":"D Turgut","year":"2020","unstructured":"Turgut, D., & Alper, K. U. (2020). A novel term weighting scheme for text classification: TF-MONO. Journal of Informetrics, 14(4), 101076.","journal-title":"Journal of Informetrics"},{"key":"4419_CR44","unstructured":"Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., & Bengio, Y. (2018). Graph attention networks. ICLR."},{"key":"4419_CR45","doi-asserted-by":"crossref","unstructured":"Wang, X., Ji, Ho., Shi, C., Wang, B., Ye, Y., Cui, P., &Yu, S. P. (2019). Heterogeneous graph attention network. WWW \u201919 (pp. 2022\u20132032).","DOI":"10.1145\/3308558.3313562"},{"key":"4419_CR46","doi-asserted-by":"crossref","unstructured":"Wang, R., Li, Z., Cao, J., Chen, T., & Wang, L. (2019). Convolutional recurrent neural networks for text classification. IJCNN, pp. 1\u20136.","DOI":"10.1109\/IJCNN.2019.8852406"},{"key":"4419_CR47","doi-asserted-by":"crossref","unstructured":"Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., Funtowicz, M., & Brew, J. (2019). Huggingface\u2019s transformers: State of the art natural language processing. arXiv preprint arXiv:1910.03771.","DOI":"10.18653\/v1\/2020.emnlp-demos.6"},{"key":"4419_CR48","unstructured":"Xu, K., Hu, W., Leskovec, J., & Jegelka, S. (2019). How powerful are graph neural networks? ICLR."},{"key":"4419_CR49","doi-asserted-by":"crossref","unstructured":"Yao, L., Mao, C., & Luo, Y. (2019). Graph convolutional networks for text classification. AAAI (pp. 7370\u20137377).","DOI":"10.1609\/aaai.v33i01.33017370"},{"issue":"1","key":"4419_CR50","doi-asserted-by":"publisher","first-page":"399","DOI":"10.1007\/s11192-019-03206-9","volume":"121","author":"Y Zhang","year":"2019","unstructured":"Zhang, Y., Zhao, F., & Lu, J. (2019). P2v: Large-scale academic paper embedding. Scientometrics, 121(1), 399\u2013432.","journal-title":"Scientometrics"},{"key":"4419_CR51","unstructured":"Zhang, T., Kishore, V., Wu, F., Weinberger, Q. K., & Artzi, Y. (2020). BERTScore: Evaluating text generation with BERT. ICLR."},{"key":"4419_CR52","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Yu, X., Cui, Z., Wu, S., Wen, Z., & Wang, L. (2020). Every document owns its structure: Inductive text classification via graph neural networks. ACL.","DOI":"10.18653\/v1\/2020.acl-main.31"},{"key":"4419_CR53","doi-asserted-by":"crossref","unstructured":"Zhang, C., Song, D., Huang, C., Swami, A., & Chawla, V. N. (2019). Heterogeneous graph neural network. KDD (pp. 793\u2013803).","DOI":"10.1145\/3292500.3330961"},{"key":"4419_CR54","doi-asserted-by":"crossref","unstructured":"Zhang, M., Gao, X., Cao, D. M., & Ma, Y. (2006). Modelling citation networks for improving scientific paper classification performance. PRICAI (pp. 413\u2013422).","DOI":"10.1007\/978-3-540-36668-3_45"}],"container-title":["Scientometrics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11192-022-04419-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11192-022-04419-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11192-022-04419-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,8,10]],"date-time":"2022-08-10T07:43:00Z","timestamp":1660117380000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11192-022-04419-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,11]]},"references-count":54,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2022,8]]}},"alternative-id":["4419"],"URL":"https:\/\/doi.org\/10.1007\/s11192-022-04419-1","relation":{},"ISSN":["0138-9130","1588-2861"],"issn-type":[{"value":"0138-9130","type":"print"},{"value":"1588-2861","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,11]]},"assertion":[{"value":"23 December 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 May 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 July 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}