{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T23:37:09Z","timestamp":1782517029547,"version":"3.54.5"},"reference-count":73,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2024,8,12]],"date-time":"2024-08-12T00:00:00Z","timestamp":1723420800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2024,8,12]],"date-time":"2024-08-12T00:00:00Z","timestamp":1723420800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Artif Intell Rev"],"DOI":"10.1007\/s10462-024-10885-1","type":"journal-article","created":{"date-parts":[[2024,8,11]],"date-time":"2024-08-11T23:10:48Z","timestamp":1723417848000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Link prediction for hypothesis generation: an active curriculum learning infused temporal graph-based approach"],"prefix":"10.1007","volume":"57","author":[{"given":"Uchenna","family":"Akujuobi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Priyadarshini","family":"Kumari","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jihun","family":"Choi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Samy","family":"Badreddine","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kana","family":"Maruyama","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sucheendra K.","family":"Palaniappan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tarek R.","family":"Besold","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,8,12]]},"reference":[{"key":"10885_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ins.2016.09.029","volume":"374","author":"NM Ahmed","year":"2016","unstructured":"Ahmed NM, Chen L, Wang Y et\u00a0al. (2016) Sampling-based algorithm for link prediction in temporal networks. Inform Sci 374:1\u201314","journal-title":"Inform Sci"},{"key":"10885_CR2","first-page":"4597","volume":"33","author":"U Akujuobi","year":"2020","unstructured":"Akujuobi U, Chen J, Elhoseiny M et\u00a0al. (2020) Temporal positive-unlabeled learning for biomedical hypothesis generation via risk estimation. Adv Neural Inform Proc Syst 33:4597\u20134609","journal-title":"Adv Neural Inform Proc Syst"},{"issue":"6","key":"10885_CR3","first-page":"2988","volume":"34","author":"U Akujuobi","year":"2020","unstructured":"Akujuobi U, Spranger M, Palaniappan SK et\u00a0al. (2020) T-pair: Temporal node-pair embedding for automatic biomedical hypothesis generation. IEEE Trans Knowledge Data Eng 34(6):2988\u20133001","journal-title":"IEEE Trans Knowledge Data Eng"},{"issue":"suppl\u20133","key":"10885_CR4","first-page":"581S","volume":"125","author":"RL Anderson","year":"1995","unstructured":"Anderson RL, Wolf WJ (1995) Compositional changes in trypsin inhibitors, phytic acid, saponins and isoflavones related to soybean processing. J Nutr 125(suppl\u20133):581S-588S","journal-title":"J Nutr"},{"key":"10885_CR5","unstructured":"Arthur D, Vassilvitskii S (2006) $$k$$-means++: The advantages of careful seeding. Stanford University, Tech. rep"},{"key":"10885_CR6","volume-title":"Deep batch active learning by diverse, uncertain gradient lower bounds","author":"JT Ash","year":"2020","unstructured":"Ash JT, Zhang C, Krishnamurthy A et\u00a0al. (2020) Deep batch active learning by diverse, uncertain gradient lower bounds. ICLR, Vienna"},{"issue":"7","key":"10885_CR7","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0180539","volume":"12","author":"SH Baek","year":"2017","unstructured":"Baek SH, Lee D, Kim M et\u00a0al. (2017) Enriching plausible new hypothesis generation in pubmed. PloS One 12(7):e0180539","journal-title":"PloS One"},{"key":"10885_CR8","doi-asserted-by":"crossref","unstructured":"Bengio Y, Louradour J, Collobert R, et\u00a0al. (2009) Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, 41\u201348","DOI":"10.1145\/1553374.1553380"},{"key":"10885_CR9","doi-asserted-by":"crossref","unstructured":"Brainard J (2020) Scientists are drowning in COVID-19 papers. Can new tools keep them afloat? \u2014 science.org. https:\/\/www.science.org\/content\/article\/scientists-are-drowning-covid-19-papers-can-new-tools-keep-them-afloat, [Accessed 25-May-2023]","DOI":"10.1126\/science.abc7839"},{"issue":"5","key":"10885_CR10","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1037\/h0046049","volume":"63","author":"D Cartwright","year":"1956","unstructured":"Cartwright D, Harary F (1956) Structural balance: a generalization of Heider\u2019s theory. Psychol Rev 63(5):277","journal-title":"Psychol Rev"},{"key":"10885_CR11","unstructured":"Chen T, Kornblith S, Norouzi M, et\u00a0al. (2020) A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, PMLR, 1597\u20131607"},{"issue":"1","key":"10885_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12859-018-2163-9","volume":"19","author":"G Crichton","year":"2018","unstructured":"Crichton G, Guo Y, Pyysalo S et\u00a0al. (2018) Neural networks for link prediction in realistic biomedical graphs: a multi-dimensional evaluation of graph embedding-based approaches. BMC Bioinform 19(1):1\u201311","journal-title":"BMC Bioinform"},{"issue":"10","key":"10885_CR13","doi-asserted-by":"publisher","first-page":"882","DOI":"10.18410\/jebmh\/2018\/179","volume":"5","author":"A Deepika","year":"2018","unstructured":"Deepika A (2018) Effect of flaxseed oil in plaque induced gingivitis-a randomized control double-blind study. J Evid Based Med Healthc 5(10):882\u20135","journal-title":"J Evid Based Med Healthc"},{"key":"10885_CR14","unstructured":"Fan Jw, Lussier YA (2017) Word-of-mouth innovation: hypothesis generation for supplement repurposing based on consumer reviews. In: AMIA Annual Symposium Proceedings, American Medical Informatics Association, p 689"},{"key":"10885_CR15","volume-title":"Query by committee made real","author":"R Gilad-Bachrach","year":"2006","unstructured":"Gilad-Bachrach R, Navot A, Tishby N (2006) Query by committee made real. NeurIPS, Denver"},{"key":"10885_CR16","unstructured":"Gitmez AA, Z\u00e1rate RA (2022) Proximity, similarity, and friendship formation: Theory and evidence. arXiv preprint arXiv:2210.06611"},{"key":"10885_CR17","unstructured":"Gopalakrishnan V, Jha K, Zhang A, et\u00a0al. (2016) Generating hypothesis: Using global and local features in graph to discover new knowledge from medical literature. In: Proceedings of the 8th International Conference on Bioinformatics and Computational Biology, BICOB, 23\u201330"},{"key":"10885_CR18","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, 855\u2013864","DOI":"10.1145\/2939672.2939754"},{"key":"10885_CR19","unstructured":"Hacohen G, Weinshall D (2019) On the power of curriculum learning in training deep networks. In: International Conference on Machine Learning, PMLR, 2535\u20132544"},{"key":"10885_CR20","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1706.02216","author":"W Hamilton","year":"2017","unstructured":"Hamilton W, Ying Z, Leskovec J (2017) Inductive representation learning on large graphs. Adv Neural Inform Proc Syst. https:\/\/doi.org\/10.48550\/arXiv.1706.02216","journal-title":"Adv Neural Inform Proc Syst"},{"key":"10885_CR21","unstructured":"Hendrycks D, Gimpel K (2016) Bridging nonlinearities and stochastic regularizers with gaussian error linear units. CoRR, abs\/160608415 3"},{"key":"10885_CR22","doi-asserted-by":"crossref","unstructured":"Hisano R (2018) Semi-supervised graph embedding approach to dynamic link prediction. In: Complex Networks IX: Proceedings of the 9th Conference on Complex Networks CompleNet 2018 9, Springer, 109\u2013121","DOI":"10.1007\/978-3-319-73198-8_10"},{"key":"10885_CR23","unstructured":"Hristovski D, Friedman C, Rindflesch TC, et\u00a0al. (2006) Exploiting semantic relations for literature-based discovery. In: AMIA Annual Symposium Proceedings, 349"},{"key":"10885_CR24","doi-asserted-by":"crossref","unstructured":"Jha K, Xun G, Wang Y, et\u00a0al. (2019) Hypothesis generation from text based on co-evolution of biomedical concepts. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, ACM, 843\u2013851","DOI":"10.1145\/3292500.3330977"},{"key":"10885_CR25","unstructured":"Kazemi SM, Goel R, Eghbali S, et\u00a0al. (2019) Time2vec: Learning a vector representation of time. arXiv preprint arXiv:1907.05321"},{"issue":"6971","key":"10885_CR26","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1038\/nature02236","volume":"427","author":"RD King","year":"2004","unstructured":"King RD, Whelan KE, Jones FM et\u00a0al. (2004) Functional genomic hypothesis generation and experimentation by a robot scientist. Nature 427(6971):247\u2013252","journal-title":"Nature"},{"issue":"5923","key":"10885_CR27","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1126\/science.1165620","volume":"324","author":"RD King","year":"2009","unstructured":"King RD, Rowland J, Oliver SG et\u00a0al. (2009) The automation of science. Science 324(5923):85\u201389","journal-title":"Science"},{"key":"10885_CR28","volume-title":"BatchBALD: efficient and diverse batch acquisition for deep Bayesian active learning","author":"A Kirsch","year":"2019","unstructured":"Kirsch A, van Amersfoort J, Gal Y (2019) BatchBALD: efficient and diverse batch acquisition for deep Bayesian active learning. NeurIPS, Denver"},{"issue":"1","key":"10885_CR29","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1038\/s41540-021-00189-3","volume":"7","author":"H Kitano","year":"2021","unstructured":"Kitano H (2021) Nobel turing challenge: creating the engine for scientific discovery. npj Syst Biol Appl 7(1):29","journal-title":"npj Syst Biol Appl"},{"issue":"suppl 6","key":"10885_CR30","doi-asserted-by":"publisher","first-page":"1025","DOI":"10.1289\/ehp.02110s61025","volume":"110","author":"MT Klein","year":"2002","unstructured":"Klein MT, Hou G, Quann RJ et\u00a0al. (2002) Biomol: a computer-assisted biological modeling tool for complex chemical mixtures and biological processes at the molecular level. Environ Health Perspect 110(suppl 6):1025\u20131029","journal-title":"Environ Health Perspect"},{"issue":"11","key":"10885_CR31","doi-asserted-by":"publisher","first-page":"1326","DOI":"10.1038\/s42256-023-00735-0","volume":"5","author":"M Krenn","year":"2023","unstructured":"Krenn M, Buffoni L, Coutinho B et\u00a0al. (2023) Forecasting the future of artificial intelligence with machine learning-based link prediction in an exponentially growing knowledge network. Nat Machine Intell 5(11):1326\u20131335","journal-title":"Nat Machine Intell"},{"key":"10885_CR32","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2020\/312","volume-title":"Batch decorrelation for active metric learning","author":"P Kumari","year":"2020","unstructured":"Kumari P, Goru R, Chaudhuri S et\u00a0al. (2020) Batch decorrelation for active metric learning. IJCAI-PRICAI, Jeju Island"},{"key":"10885_CR33","doi-asserted-by":"crossref","unstructured":"Kumar S, Zhang X, Leskovec J (2019) Predicting dynamic embedding trajectory in temporal interaction networks. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 1269\u20131278","DOI":"10.1145\/3292500.3330895"},{"key":"10885_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.foodchem.2022.132715","volume":"385","author":"Y Liu","year":"2022","unstructured":"Liu Y, Liu Y, Li P et\u00a0al. (2022) Antibacterial properties of cyclolinopeptides from flaxseed oil and their application on beef. Food Chem 385:132715","journal-title":"Food Chem"},{"issue":"2","key":"10885_CR35","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1093\/jn\/119.2.211","volume":"119","author":"B L\u00f6nnerdal","year":"1989","unstructured":"L\u00f6nnerdal B, Sandberg AS, Sandstr\u00f6m B et\u00a0al. (1989) Inhibitory effects of phytic acid and other inositol phosphates on zinc and calcium absorption in suckling rats. J Nutr 119(2):211\u2013214","journal-title":"J Nutr"},{"key":"10885_CR36","unstructured":"Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101"},{"key":"10885_CR37","doi-asserted-by":"crossref","unstructured":"Milani\u00a0Fard A, Bagheri E, Wang K (2019) Relationship prediction in dynamic heterogeneous information networks. In: Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14\u201318, 2019, Proceedings, Part I 41, Springer, 19\u201334","DOI":"10.1007\/978-3-030-15712-8_2"},{"key":"10885_CR38","first-page":"969","volume":"2018","author":"GH Nguyen","year":"2018","unstructured":"Nguyen GH, Lee JB, Rossi RA et\u00a0al. (2018) Continuous-time dynamic network embeddings. Companion Proc Web Conf 2018:969\u2013976","journal-title":"Companion Proc Web Conf"},{"key":"10885_CR39","doi-asserted-by":"crossref","unstructured":"Pareja A, Domeniconi G, Chen J, et\u00a0al. (2020) Evolvegcn: Evolving graph convolutional networks for dynamic graphs. In: Proceedings of the AAAI conference on artificial intelligence, 5363\u20135370","DOI":"10.1609\/aaai.v34i04.5984"},{"key":"10885_CR40","volume-title":"Bayesian batch active learning as sparse subset approximation","author":"R Pinsler","year":"2019","unstructured":"Pinsler R, Gordon J, Nalisnick E et\u00a0al. (2019) Bayesian batch active learning as sparse subset approximation. NeurIPS, Denver"},{"key":"10885_CR41","doi-asserted-by":"crossref","unstructured":"Priyadarshini K, Chaudhuri S, Borkar V, et\u00a0al. (2021) A unified batch selection policy for active metric learning. In: Machine Learning and Knowledge Discovery in Databases. Research Track: European Conference, ECML PKDD 2021, Bilbao, Spain, September 13\u201317, 2021, Proceedings, Part II 21, Springer, 599\u2013616","DOI":"10.1007\/978-3-030-86520-7_37"},{"key":"10885_CR42","unstructured":"Rossi E, Chamberlain B, Frasca F, et\u00a0al. (2020) Temporal graph networks for deep learning on dynamic graphs. arXiv preprint arXiv:2006.10637"},{"issue":"15","key":"10885_CR43","doi-asserted-by":"publisher","first-page":"2668","DOI":"10.1016\/j.phytochem.2008.08.023","volume":"69","author":"K Schullehner","year":"2008","unstructured":"Schullehner K, Dick R, Vitzthum F et\u00a0al. (2008) Benzoxazinoid biosynthesis in dicot plants. Phytochemistry 69(15):2668\u20132677","journal-title":"Phytochemistry"},{"key":"10885_CR44","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-01560-1","volume-title":"Active learning","author":"B Settles","year":"2012","unstructured":"Settles B (2012) Active learning. SLAIML, Shimla"},{"key":"10885_CR45","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1016\/j.socnet.2015.02.006","volume":"43","author":"F Shi","year":"2015","unstructured":"Shi F, Foster JG, Evans JA (2015) Weaving the fabric of science: dynamic network models of science\u2019s unfolding structure. Soc Networks 43:73\u201385","journal-title":"Soc Networks"},{"key":"10885_CR46","doi-asserted-by":"crossref","unstructured":"Singer U, Guy I, Radinsky K (2019) Node embedding over temporal graphs. arXiv preprint arXiv:1903.08889","DOI":"10.24963\/ijcai.2019\/640"},{"issue":"3","key":"10885_CR47","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1016\/S0169-2607(98)00033-9","volume":"57","author":"NR Smalheiser","year":"1998","unstructured":"Smalheiser NR, Swanson DR (1998) Using Arrowsmith: a computer-assisted approach to formulating and assessing scientific hypotheses. Comput Methods Prog Biomed 57(3):149\u2013153","journal-title":"Comput Methods Prog Biomed"},{"key":"10885_CR48","doi-asserted-by":"publisher","DOI":"10.1201\/b18958","volume-title":"Accelerating discovery: mining unstructured information for hypothesis generation","author":"S Spangler","year":"2015","unstructured":"Spangler S (2015) Accelerating discovery: mining unstructured information for hypothesis generation. Chapman and Hall\/CRC, Boca Raton"},{"key":"10885_CR49","doi-asserted-by":"crossref","unstructured":"Spangler S, Wilkins AD, Bachman BJ, et\u00a0al. (2014) Automated hypothesis generation based on mining scientific literature. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, 1877\u20131886","DOI":"10.1145\/2623330.2623667"},{"key":"10885_CR50","doi-asserted-by":"crossref","unstructured":"Srihari RK, Xu L, Saxena T (2007) Use of ranked cross document evidence trails for hypothesis generation. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, 677\u2013686","DOI":"10.1145\/1281192.1281265"},{"issue":"1","key":"10885_CR51","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava N, Hinton G, Krizhevsky A et\u00a0al. (2014) Dropout: a simple way to prevent neural networks from overfitting. J Machine Learn Res 15(1):1929\u20131958","journal-title":"J Machine Learn Res"},{"issue":"1","key":"10885_CR52","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1353\/pbm.1986.0087","volume":"30","author":"DR Swanson","year":"1986","unstructured":"Swanson DR (1986) Fish oil, Raynaud\u2019s syndrome, and undiscovered public knowledge. Perspect Biol Med 30(1):7\u201318","journal-title":"Perspect Biol Med"},{"issue":"2","key":"10885_CR53","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1016\/S0004-3702(97)00008-8","volume":"91","author":"DR Swanson","year":"1997","unstructured":"Swanson DR, Smalheiser NR (1997) An interactive system for finding complementary literatures: a stimulus to scientific discovery. Artif Intell 91(2):183\u2013203","journal-title":"Artif Intell"},{"key":"10885_CR54","doi-asserted-by":"crossref","unstructured":"Sybrandt J, Shtutman M, Safro I (2017) Moliere: Automatic biomedical hypothesis generation system. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1633\u20131642","DOI":"10.1145\/3097983.3098057"},{"key":"10885_CR55","doi-asserted-by":"crossref","unstructured":"Sybrandt J, Tyagin I, Shtutman M, et\u00a0al. (2020) Agatha: automatic graph mining and transformer based hypothesis generation approach. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management, 2757\u20132764","DOI":"10.1145\/3340531.3412684"},{"key":"10885_CR56","volume-title":"Computer-assisted research design and analysis","author":"BG Tabachnick","year":"2000","unstructured":"Tabachnick BG, Fidell LS (2000) Computer-assisted research design and analysis. Allyn & Bacon Inc, Boston"},{"key":"10885_CR57","unstructured":"Trautman A (2022) Nutritive knowledge based discovery: Enhancing precision nutrition hypothesis generation. PhD thesis, The University of North Carolina at Charlotte"},{"key":"10885_CR58","unstructured":"Trivedi R, Farajtabar M, Biswal P, et\u00a0al. (2019) Dyrep: Learning representations over dynamic graphs. In: International Conference on Learning Representations"},{"issue":"11","key":"10885_CR59","first-page":"2579","volume":"9","author":"L Van der Maaten","year":"2008","unstructured":"Van der Maaten L, Hinton G (2008) Visualizing data using t-sne. J Machine Learn Res 9(11):2579\u20132605","journal-title":"J Machine Learn Res"},{"key":"10885_CR60","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1706.03762","author":"A Vaswani","year":"2017","unstructured":"Vaswani A, Shazeer N, Parmar N et\u00a0al. (2017) Attention is all you need. Adv Neural Inform Proc Syst. https:\/\/doi.org\/10.48550\/arXiv.1706.03762","journal-title":"Adv Neural Inform Proc Syst"},{"key":"10885_CR61","first-page":"1238","volume":"2021","author":"Y Wang","year":"2021","unstructured":"Wang Y, Wang W, Liang Y et\u00a0al. (2021) Curgraph: curriculum learning for graph classification. Proc Web Conf 2021:1238\u20131248","journal-title":"Proc Web Conf"},{"key":"10885_CR62","doi-asserted-by":"publisher","first-page":"694","DOI":"10.1007\/978-3-031-15931-2_57","volume-title":"Artificial Neural Networks and Machine Learning-ICANN 2022: 31st International Conference on Artificial Neural Networks, Bristol, UK, September 6\u20139, 2022, Proceedings","author":"Z Wang","year":"2022","unstructured":"Wang Z, Li Q, Yu D et\u00a0al. (2022) Temporal graph transformer for dynamic network. In: Part II (ed) Artificial Neural Networks and Machine Learning-ICANN 2022: 31st International Conference on Artificial Neural Networks, Bristol, UK, September 6\u20139, 2022, Proceedings. Springer, Cham, pp 694\u2013705"},{"key":"10885_CR63","unstructured":"Wang L, Chang X, Li S, et\u00a0al. (2021a) Tcl: Transformer-based dynamic graph modelling via contrastive learning. arXiv preprint arXiv:2105.07944"},{"issue":"1","key":"10885_CR64","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1186\/s13326-015-0021-5","volume":"6","author":"D Weissenborn","year":"2015","unstructured":"Weissenborn D, Schroeder M, Tsatsaronis G (2015) Discovering relations between indirectly connected biomedical concepts. J Biomed Semant 6(1):28","journal-title":"J Biomed Semant"},{"key":"10885_CR65","doi-asserted-by":"crossref","unstructured":"Wen Y, Zhang K, Li Z, et\u00a0al. (2016) A discriminative feature learning approach for deep face recognition. In: Computer Vision\u2013ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11\u201314, 2016, Proceedings, Part VII 14, Springer, 499\u2013515","DOI":"10.1007\/978-3-319-46478-7_31"},{"key":"10885_CR66","unstructured":"White K (2021) Publications Output: U.S. Trends and International Comparisons | NSF - National Science Foundation \u2014 ncses.nsf.gov. https:\/\/ncses.nsf.gov\/pubs\/nsb20214, [Accessed 25-May-2023]"},{"key":"10885_CR67","doi-asserted-by":"crossref","unstructured":"Xun G, Jha K, Gopalakrishnan V, et\u00a0al. (2017) Generating medical hypotheses based on evolutionary medical concepts. In: 2017 IEEE International Conference on Data Mining (ICDM), IEEE, 535\u2013544","DOI":"10.1109\/ICDM.2017.63"},{"issue":"1","key":"10885_CR68","doi-asserted-by":"publisher","first-page":"16585","DOI":"10.1038\/s41598-022-21168-6","volume":"12","author":"R Zhang","year":"2022","unstructured":"Zhang R, Wang Q, Yang Q et\u00a0al. (2022) Temporal link prediction via adjusted sigmoid function and 2-simplex structure. Sci Rep 12(1):16585","journal-title":"Sci Rep"},{"key":"10885_CR69","doi-asserted-by":"crossref","unstructured":"Zhang Y, Pang J (2015) Distance and friendship: A distance-based model for link prediction in social networks. In: Asia-Pacific Web Conference, Springer, 55\u201366","DOI":"10.1007\/978-3-319-25255-1_5"},{"key":"10885_CR70","unstructured":"Zhang Z, Wang J, Zhao L (2023) Relational curriculum learning for graph neural networks. https:\/\/openreview.net\/forum?id=1bLT3dGNS0"},{"key":"10885_CR71","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1016\/j.aej.2022.08.010","volume":"63","author":"Y Zhong","year":"2023","unstructured":"Zhong Y, Huang C (2023) A dynamic graph representation learning based on temporal graph transformer. Alexandria Eng J 63:359\u2013369","journal-title":"Alexandria Eng J"},{"issue":"23","key":"10885_CR72","doi-asserted-by":"publisher","first-page":"5253","DOI":"10.1093\/bioinformatics\/btac660","volume":"38","author":"H Zhou","year":"2022","unstructured":"Zhou H, Jiang H, Yao W et\u00a0al. (2022) Learning temporal difference embeddings for biomedical hypothesis generation. Bioinformatics 38(23):5253\u20135261","journal-title":"Bioinformatics"},{"key":"10885_CR73","doi-asserted-by":"crossref","unstructured":"Zhou L, Yang Y, Ren X, et\u00a0al. (2018) Dynamic network embedding by modeling triadic closure process. In: Proceedings of the AAAI Conference on Artificial Intelligence","DOI":"10.1609\/aaai.v32i1.11257"}],"container-title":["Artificial Intelligence Review"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-024-10885-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10462-024-10885-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-024-10885-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,5]],"date-time":"2024-09-05T01:22:20Z","timestamp":1725499340000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10462-024-10885-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,12]]},"references-count":73,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["10885"],"URL":"https:\/\/doi.org\/10.1007\/s10462-024-10885-1","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-3564716\/v1","asserted-by":"object"}]},"ISSN":["1573-7462"],"issn-type":[{"value":"1573-7462","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,12]]},"assertion":[{"value":"25 July 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 August 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no Conflict of interest to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"244"}}