{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T04:28:14Z","timestamp":1772252894624,"version":"3.50.1"},"reference-count":66,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2023,1,11]],"date-time":"2023-01-11T00:00:00Z","timestamp":1673395200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"NSF","doi-asserted-by":"publisher","award":["DMR 1933525"],"award-info":[{"award-number":["DMR 1933525"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"NSF","doi-asserted-by":"publisher","award":["OAC 1920147"],"award-info":[{"award-number":["OAC 1920147"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Informatics"],"abstract":"<jats:p>This work considers the task of representation learning on the attributed relational graph (ARG). Both the nodes and edges in an ARG are associated with attributes\/features allowing ARGs to encode rich structural information widely observed in real applications. Existing graph neural networks offer limited ability to capture complex interactions within local structural contexts, which hinders them from taking advantage of the expression power of ARGs. We propose motif convolution module (MCM), a new motif-based graph representation learning technique to better utilize local structural information. The ability to handle continuous edge and node features is one of MCM\u2019s advantages over existing motif-based models. MCM builds a motif vocabulary in an unsupervised way and deploys a novel motif convolution operation to extract the local structural context of individual nodes, which is then used to learn higher level node representations via multilayer perceptron and\/or message passing in graph neural networks. When compared with other graph learning approaches to classifying synthetic graphs, our approach is substantially better at capturing structural context. We also demonstrate the performance and explainability advantages of our approach by applying it to several molecular benchmarks.<\/jats:p>","DOI":"10.3390\/informatics10010008","type":"journal-article","created":{"date-parts":[[2023,1,11]],"date-time":"2023-01-11T05:26:31Z","timestamp":1673414791000},"page":"8","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Motif-Based Graph Representation Learning with Application to Chemical Molecules"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8295-5534","authenticated-orcid":false,"given":"Yifei","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Computer Science, Brandeis University, Waltham, MA 02453, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2626-7865","authenticated-orcid":false,"given":"Shiyang","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ 07030, USA"}]},{"given":"Guobin","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Brandeis University, Waltham, MA 02453, USA"}]},{"given":"Ethan","family":"Shurberg","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Brandeis University, Waltham, MA 02453, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6323-7388","authenticated-orcid":false,"given":"Hang","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ 07030, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3177-2754","authenticated-orcid":false,"given":"Pengyu","family":"Hong","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Brandeis University, Waltham, MA 02453, USA"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,11]]},"reference":[{"key":"ref_1","first-page":"377","article-title":"Relational descriptions in picture processing","volume":"6","author":"Barrow","year":"1971","journal-title":"Mach. Intell."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"757","DOI":"10.1109\/TSMC.1979.4310127","article-title":"Error-correcting isomorphisms of attributed relational graphs for pattern analysis","volume":"9","author":"Tsai","year":"1979","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"715","DOI":"10.1021\/ci940128y","article-title":"A neural device for searching direct correlations between structures and properties of chemical compounds","volume":"37","author":"Baskin","year":"1997","journal-title":"J. Chem. Inf. Comput. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"714","DOI":"10.1109\/72.572108","article-title":"Supervised neural networks for the classification of structures","volume":"8","author":"Sperduti","year":"1997","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_5","unstructured":"Gori, M., Monfardini, G., and Scarselli, F. (August, January 31). A new model for learning in graph domains. Proceedings of the IEEE International Joint Conference on Neural Networks, Montreal, QC, Canada."},{"key":"ref_6","unstructured":"Scarselli, F., Yong, S.L., Gori, M., Hagenbuchner, M., Tsoi, A.C., and Maggini, M. (2005, January 19\u201322). Graph neural networks for ranking web pages. Proceedings of the 2005 IEEE\/WIC\/ACM International Conference on Web Intelligence, Washington, DC, USA."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_8","unstructured":"Bruna, J., Zaremba, W., Szlam, A., and LeCun, Y. (2014, January 14\u201316). Spectral networks and locally connected networks on graphs. Proceedings of the International Conference on Learning Representations, Banff, AB, Canada."},{"key":"ref_9","unstructured":"Henaff, M., Bruna, J., and LeCun, Y. (2015). Deep Convolutional Networks on Graph-Structured Data. arXiv."},{"key":"ref_10","unstructured":"Duvenaud, D.K., Maclaurin, D., Iparraguirre, J., Bombarell, R., Hirzel, T., Aspuru-Guzik, A., and Adams, R.P. (2015, January 7\u201312). Convolutional networks on graphs for learning molecular fingerprints. Proceedings of the Advances in Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_11","unstructured":"Defferrard, M., Bresson, X., and Vandergheynst, P. (2016, January 5\u201310). Convolutional neural networks on graphs with fast localized spectral filtering. Proceedings of the Advances in Neural Information Processing Systems, Barcelona, Spain."},{"key":"ref_12","unstructured":"Li, Y., Tarlow, D., Brockschmidt, M., and Zemel, R. (2016, January 2\u20134). Gated graph sequence neural networks. Proceedings of the International Conference on Learning Representations, San Juan, Puerto Rico."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Monti, F., Boscaini, D., Masci, J., Rodola, E., Svoboda, J., and Bronstein, M.M. (2017, January 21\u201326). Geometric deep learning on graphs and manifolds using mixture model cnns. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.576"},{"key":"ref_14","unstructured":"Chang, M.B., Ullman, T., Torralba, A., and Tenenbaum, J.B. (2017, January 24\u201326). A Compositional Object-Based Approach to Learning Physical Dynamics. Proceedings of the International Conference on Learning Representations, Toulon, France."},{"key":"ref_15","unstructured":"Gilmer, J., Schoenholz, S., Riley, P.F., Vinyals, O., and Dahl, G. (2017, January 6\u201311). Neural Message Passing for Quantum Chemistry. Proceedings of the International Conference on Machine Learning, Sydney, Australia."},{"key":"ref_16","unstructured":"Chang, J., Gu, J., Wang, L., Meng, G., Xiang, S., and Pan, C. (2018, January 3\u20138). Structure-aware convolutional neural networks. Proceedings of the Neural Information Processing Systems, Montr\u00e9al, QC, Canada."},{"key":"ref_17","unstructured":"Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., and Bengio, Y. (May, January 30). Graph attention networks. Proceedings of the International Conference on Learning Representations, Vancouver, BC, Canada."},{"key":"ref_18","unstructured":"Xu, K., Li, C., Tian, Y., Sonobe, T., Kawarabayashi, K., and Jegelka, S. (2018, January 10\u201315). Representation learning on graphs with jumping knowledge networks. Proceedings of the International Conference on Machine Learning, Stockholm, Sweden."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/sdata.2014.22","article-title":"Quantum chemistry structures and properties of 134 kilo molecules","volume":"1","author":"Ramakrishnan","year":"2014","journal-title":"Sci. Data"},{"key":"ref_20","unstructured":"Sch\u00fctt, K., Kindermans, P.J., Sauceda Felix, H.E., Chmiela, S., Tkatchenko, A., and M\u00fcller, K.R. (2017, January 4\u20139). Schnet: A continuous-filter convolutional neural network for modeling quantum interactions. Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_21","unstructured":"Lu, C., Liu, Q., Wang, C., Huang, Z., Lin, P., and He, L. (February, January 27). Molecular property prediction: A multilevel quantum interactions modeling perspective. Proceedings of the AAAI Conference on Artificial Intelligence, Honolulu, HI, USA."},{"key":"ref_22","unstructured":"Klicpera, J., Gro\u00df, J., and G\u00fcnnemann, S. (2020, January 26\u201330). Directional message passing for molecular graphs. Proceedings of the International Conference on Learning Representations, Addis Ababa, Ethiopia."},{"key":"ref_23","unstructured":"Klicpera, J., Giri, S., Margraf, J.T., and G\u00fcnnemann, S. (2020, January 12). Fast and uncertainty-aware directional message passing for non-equilibrium molecules. Proceedings of the Machine Learning for Molecules Workshop, Neural Information Processing Systems, Online."},{"key":"ref_24","unstructured":"Zhang, S., Liu, Y., and Xie, L. (2020, January 6\u201312). Molecular Mechanics-Driven Graph Neural Network with Multiplex Graph for Molecular Structures. Proceedings of the Machine Learning for Structural Biology Workshop at the 34th Conference on Neural Information Processing Systems, Online."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1007\/BF02289588","article-title":"Hierarchical clustering schemes","volume":"32","author":"Johnson","year":"1967","journal-title":"Psychometrika"},{"key":"ref_26","first-page":"2349","article-title":"Orange: Data Mining Toolbox in Python","volume":"14","author":"Curk","year":"2013","journal-title":"J. Mach. Learn. Res."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1109\/34.491619","article-title":"A graduated assignment algorithm for graph matching","volume":"18","author":"Gold","year":"1996","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Menke, J., and Yang, A.Y. (2020, January 25\u201329). Graduated Assignment Graph Matching for Realtime Matching of Image Wireframes. Proceedings of the 2020 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, USA.","DOI":"10.1109\/IROS45743.2020.9341237"},{"key":"ref_29","unstructured":"Wang, M., Zheng, D., Ye, Z., Gan, Q., Li, M., Song, X., Zhou, J., Ma, C., Yu, L., and Gai, Y. (2019). Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks. arXiv."},{"key":"ref_30","unstructured":"Kipf, T.N., and Welling, M. (2017). Semi-Supervised Classification with Graph Convolutional Networks. arXiv."},{"key":"ref_31","unstructured":"Xu, K., Hu, W., Leskovec, J., and Jegelka, S. How Powerful are Graph Neural Networks? In Proceedings of the International Conference on Learning Representations, Vancouver, BC, Canada, 30 April\u20133 May 2018."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1039\/C7SC02664A","article-title":"MoleculeNet: A benchmark for molecular machine learning","volume":"9","author":"Wu","year":"2018","journal-title":"Chem. Sci."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Subramonian, A. (2021, January 2\u20139). MOTIF-Driven Contrastive Learning of Graph Representations. Proceedings of the AAAI Conference on Artificial Intelligence, Virtually.","DOI":"10.1609\/aaai.v35i18.17986"},{"key":"ref_34","unstructured":"Zhang, Z., Liu, Q., Wang, H., Lu, C., and Lee, C.K. (2021, January 6\u201314). Motif-based Graph Self-Supervised Learning for Molecular Property Prediction. Proceedings of the 35th Conference on Advances in Neural Information Processing Systems, Online."},{"key":"ref_35","first-page":"4","article-title":"Rdkit documentation","volume":"1","author":"Landrum","year":"2013","journal-title":"Release"},{"key":"ref_36","unstructured":"Hu, W., Liu, B., Gomes, J., Zitnik, M., Liang, P., Pande, V., and Leskovec, J. (2019, January 6\u20139). Strategies for Pre-training Graph Neural Networks. Proceedings of the International Conference on Learning Representations, New Orleans, LA, USA."},{"key":"ref_37","unstructured":"Ramsundar, B., Eastman, P., Walters, P., and Pande, V. (2019). Deep Learning for the Life Sciences: Applying Deep Learning to Genomics, Microscopy, Drug Discovery, and More, O\u2019Reilly Media."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"5255","DOI":"10.1021\/acs.jctc.7b00577","article-title":"Prediction errors of molecular machine learning models lower than hybrid DFT error","volume":"13","author":"Faber","year":"2017","journal-title":"J. Chem. Theory Comput."},{"key":"ref_39","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Hinton","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1063\/1.1733694","article-title":"Problem of the Lengths and Strengths of Carbon\u2014Fluorine Bonds","volume":"38","author":"Peters","year":"1963","journal-title":"J. Chem. Phys."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Perozzi, B., Al-Rfou, R., and Skiena, S. (2014, January 24\u201327). Deepwalk: Online learning of social representations. Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY, USA.","DOI":"10.1145\/2623330.2623732"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., and Mei, Q. (2015, January 18\u201322). Line: Large-scale information network embedding. Proceedings of the 24th International Conference on World Wide Web, Florence, Italy.","DOI":"10.1145\/2736277.2741093"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Grover, A., and Leskovec, J. (2016, January 13\u201317). node2vec: Scalable Feature Learning for Networks. Proceedings of the KDD: Proceedings. International Conference on Knowledge Discovery & Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939754"},{"key":"ref_44","unstructured":"Sun, F.Y., Hoffman, J., Verma, V., and Tang, J. (2019, January 6\u20139). InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization. Proceedings of the International Conference on Learning Representations, New Orleans, LA, USA."},{"key":"ref_45","first-page":"4","article-title":"Deep Graph Infomax","volume":"2","author":"Velickovic","year":"2019","journal-title":"ICLR (Poster)"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Peng, Z., Huang, W., Luo, M., Zheng, Q., Rong, Y., Xu, T., and Huang, J. (2020, January 20\u201324). Graph representation learning via graphical mutual information maximization. Proceedings of the Web Conference 2020, Taipei, Taiwan.","DOI":"10.1145\/3366423.3380112"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Henderson, K., Gallagher, B., Eliassi-Rad, T., Tong, H., Basu, S., Akoglu, L., Koutra, D., Faloutsos, C., and Li, L. (2012, January 12\u201316). Rolx: Structural role extraction & mining in large graphs. Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Beijing, China.","DOI":"10.1145\/2339530.2339723"},{"key":"ref_48","unstructured":"Narayanan, A., Chandramohan, M., Chen, L., Liu, Y., and Saminathan, S. (2016). subgraph2vec: Learning distributed representations of rooted sub-graphs from large graphs. arXiv."},{"key":"ref_49","unstructured":"Ribeiro, L.F., Saverese, P.H., and Figueiredo, D.R. (2017, January 13\u201317). struc2vec: Learning node representations from structural identity. Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Hu, Z., Dong, Y., Wang, K., Chang, K.W., and Sun, Y. (2020, January 6\u201310). Gpt-gnn: Generative pre-training of graph neural networks. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Virtual Event.","DOI":"10.1145\/3394486.3403237"},{"key":"ref_51","unstructured":"You, Y., Chen, T., Wang, Z., and Shen, Y. (2020, January 13\u201318). When does self-supervision help graph convolutional networks?. Proceedings of the International Conference on Machine Learning, Online."},{"key":"ref_52","unstructured":"Rong, Y., Bian, Y., Xu, T., Xie, W., Wei, Y., Huang, W., and Huang, J. (2020, January 6\u201312). Self-Supervised Graph Transformer on Large-Scale Molecular Data. Proceedings of the NeurIPS, Online."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Sun, K., Lin, Z., and Zhu, Z. (2020, January 7\u201312). Multi-stage self-supervised learning for graph convolutional networks on graphs with few labeled nodes. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA.","DOI":"10.1609\/aaai.v34i04.6048"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Qiu, J., Chen, Q., Dong, Y., Zhang, J., Yang, H., Ding, M., Wang, K., and Tang, J. (2020, January 6\u201310). Gcc: Graph contrastive coding for graph neural network pre-training. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Virtual Event.","DOI":"10.1145\/3394486.3403168"},{"key":"ref_55","unstructured":"Hafidi, H., Ghogho, M., Ciblat, P., and Swami, A. (2020). Graphcl: Contrastive self-supervised learning of graph representations. arXiv."},{"key":"ref_56","unstructured":"Hassani, K., and Khasahmadi, A.H. (2020, January 13\u201318). Contrastive multi-view representation learning on graphs. Proceedings of the International Conference on Machine Learning, Virtual."},{"key":"ref_57","first-page":"5812","article-title":"Graph contrastive learning with augmentations","volume":"33","author":"You","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_58","unstructured":"Xu, M., Wang, H., Ni, B., Guo, H., and Tang, J. (2021, January 18\u201324). Self-Supervised Graph-Level Representation Learning with Local and Global Structure. Proceedings of the 38th International Conference on Machine Learning, PMLR, Virtual."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Zhao, C., Liu, S., Huang, F., Liu, S., and Zhang, W. (2021, January 19\u201326). CSGNN: Contrastive self-supervised graph neural network for molecular interaction prediction. Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, Online.","DOI":"10.24963\/ijcai.2021\/517"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"586","DOI":"10.1287\/mnsc.9.4.586","article-title":"The quadratic assignment problem","volume":"9","author":"Lawler","year":"1963","journal-title":"Manag. Sci."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"876","DOI":"10.1214\/aoms\/1177703591","article-title":"A relationship between arbitrary positive matrices and doubly stochastic matrices","volume":"35","author":"Sinkhorn","year":"1964","journal-title":"Ann. Math. Stat."},{"key":"ref_62","unstructured":"Harris, M. (2023, January 04). Optimizing Cuda. Supercomputing 2007 Tutorial, Reno, NV, USA. Available online: https:\/\/www.enseignement.polytechnique.fr\/profs\/informatique\/Eric.Goubault\/Cours09\/CUDA\/SC07_CUDA_5_Optimization_Harris.pdf."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Chen, J., Zheng, S., Song, Y., Rao, J., and Yang, Y. (2021, January 19\u201327). Learning Attributed Graph Representation with Communicative Message Passing Transformer. Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence (IJCAI), Virtual Event.","DOI":"10.24963\/ijcai.2021\/309"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Lim, S., and Lee, Y.O. (2021, January 10\u201315). Predicting chemical properties using self-attention multi-task learning based on SMILES representation. Proceedings of the 2020 25th International Conference on Pattern Recognition (ICPR), Milan, Italy.","DOI":"10.1109\/ICPR48806.2021.9412555"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"2324","DOI":"10.1021\/acs.jcim.5b00559","article-title":"ZINC 15\u2013ligand discovery for everyone","volume":"55","author":"Sterling","year":"2015","journal-title":"J. Chem. Inf. Model."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1039\/tf9474300158","article-title":"The dependence of the properties of carbonyl compounds upon polarity","volume":"43","author":"Walsh","year":"1947","journal-title":"Trans. Faraday Soc."}],"container-title":["Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2227-9709\/10\/1\/8\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:06:58Z","timestamp":1760119618000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2227-9709\/10\/1\/8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,11]]},"references-count":66,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["informatics10010008"],"URL":"https:\/\/doi.org\/10.3390\/informatics10010008","relation":{"has-preprint":[{"id-type":"doi","id":"10.20944\/preprints202212.0062.v1","asserted-by":"object"}]},"ISSN":["2227-9709"],"issn-type":[{"value":"2227-9709","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,11]]}}}