{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,13]],"date-time":"2026-06-13T01:40:32Z","timestamp":1781314832578,"version":"3.54.1"},"reference-count":179,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,11,16]],"date-time":"2023-11-16T00:00:00Z","timestamp":1700092800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Artif. Intell."],"abstract":"<jats:p>Graphs are used as a model of complex relationships among data in biological science since the advent of systems biology in the early 2000. In particular, graph data analysis and graph data mining play an important role in biology interaction networks, where recent techniques of artificial intelligence, usually employed in other type of networks (e.g., social, citations, and trademark networks) aim to implement various data mining tasks including classification, clustering, recommendation, anomaly detection, and link prediction. The commitment and efforts of artificial intelligence research in network biology are motivated by the fact that machine learning techniques are often prohibitively computational demanding, low parallelizable, and ultimately inapplicable, since biological network of realistic size is a large system, which is characterised by a high density of interactions and often with a non-linear dynamics and a non-Euclidean latent geometry. Currently, graph embedding emerges as the new learning paradigm that shifts the tasks of building complex models for classification, clustering, and link prediction to learning an informative representation of the graph data in a vector space so that many graph mining and learning tasks can be more easily performed by employing efficient non-iterative traditional models (e.g., a linear support vector machine for the classification task). The great potential of graph embedding is the main reason of the flourishing of studies in this area and, in particular, the artificial intelligence learning techniques. In this mini review, we give a comprehensive summary of the main graph embedding algorithms in light of the recent burgeoning interest in geometric deep learning.<\/jats:p>","DOI":"10.3389\/frai.2023.1256352","type":"journal-article","created":{"date-parts":[[2023,11,16]],"date-time":"2023-11-16T14:00:55Z","timestamp":1700143255000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["Graph embedding and geometric deep learning relevance to network biology and structural chemistry"],"prefix":"10.3389","volume":"6","author":[{"given":"Paola","family":"Lecca","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michela","family":"Lecca","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2023,11,16]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"257","DOI":"10.3390\/e25020257","article-title":"A novel temporal network-embedding algorithm for link prediction in dynamic networks","volume":"25","author":"Abbas","year":"2023","journal-title":"Entropy"},{"key":"B2","doi-asserted-by":"publisher","first-page":"2826","DOI":"10.1093\/bioinformatics\/bty206","article-title":"The latent geometry of the human protein interaction network","volume":"34","author":"Alanis-Lobato","year":"2018","journal-title":"Bioinformatics"},{"key":"B3","doi-asserted-by":"publisher","first-page":"100130","DOI":"10.1016\/j.mlwa.2021.100130","article-title":"Network representation learning systematic review: Ancestors and current development state","volume":"6","author":"Amara","year":"2021","journal-title":"Mach. Learn. Appl"},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-46908-4_6","article-title":"\u201cThe complexity of the graph embedding problem,\u201d","author":"Archdeacon","year":"1990","journal-title":"Topics in Combinatorics and Graph Theory"},{"key":"B5","doi-asserted-by":"publisher","first-page":"1023","DOI":"10.1038\/s42256-021-00418-8","article-title":"Geometric deep learning on molecular representations","volume":"3","author":"Atz","year":"2021","journal-title":"Nat. Mach. Intellig"},{"key":"B6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s41109-019-0156-x","article-title":"An information-theoretic, all-scales approach to comparing networks","volume":"4","author":"Bagrow","year":"2019","journal-title":"Appl. Netw. Sci"},{"key":"B7","unstructured":"\u201cAutoencoders, unsupervised learning, and deep architectures,\u201d3749\n            BaldiP.\n          Proceedings of ICML Workshop on Unsupervised and Transfer Learning, Vol. 272012"},{"key":"B8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s41109-019-0169-5","article-title":"Node embeddings in dynamic graphs","volume":"4","author":"B\u00e9res","year":"2019","journal-title":"Appl. Netw. Sci"},{"key":"B9","doi-asserted-by":"publisher","first-page":"895535","DOI":"10.3389\/fnagi.2022.895535","article-title":"Geometric deep learning reveals a structuro-temporal understanding of healthy and pathologic brain aging","volume":"14","author":"Besson","year":"2022","journal-title":"Front. Aging Neurosci"},{"key":"B10","doi-asserted-by":"publisher","first-page":"974","DOI":"10.1038\/srep41974","article-title":"Emergent hyperbolic network geometry","volume":"7","author":"Bianconi","year":"2017","journal-title":"Sci. Rep"},{"key":"B11","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1038\/s42254-020-00264-4","article-title":"Network geometry","volume":"3","author":"Bogu\u00f1\u00e1","year":"2021","journal-title":"Nat. Rev. Phys"},{"key":"B12","doi-asserted-by":"publisher","first-page":"521","DOI":"10.1103\/PhysRevLett.59.521","article-title":"Space-time as a causal set","volume":"59","author":"Bombelli","year":"1987","journal-title":"Phys. Rev. Lett"},{"key":"B13","doi-asserted-by":"publisher","DOI":"10.1561\/2200000076","article-title":"Graph kernels: State-of-the-art and future challenges","author":"Borgwardt","year":"2020","journal-title":"Trends Mach. Learn"},{"key":"B14","unstructured":"BronsteinM. M.\n            BrunaJ.\n            CohenT.\n            Veli\u010dkovi\u0107P.\n          Geometric deep learning: grids, groups, graphs, geodesics, and gauges. ArXiv. abs\/2104.134782021"},{"key":"B15","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1109\/MSP.2017.2693418","article-title":"Geometric deep learning: going beyond euclidean data","volume":"34","author":"Bronstein","year":"2017","journal-title":"IEEE Signal Process. Mag"},{"key":"B16","doi-asserted-by":"publisher","first-page":"1616","DOI":"10.1109\/TKDE.2018.2807452","article-title":"A comprehensive survey of graph embedding: problems, techniques, and applications","volume":"30","author":"Cai","year":"2018","journal-title":"IEEE Trans. Knowl. Data Eng"},{"key":"B17","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1145\/3437963.3441783","article-title":"\u201cBipartite graph embedding via mutual information maximization,\u201d","volume-title":"Proceedings of the 14th ACM International Conference on Web Search and Data Mining","author":"Cao","year":"2021"},{"key":"B18","first-page":"891","article-title":"\u201cGrarep: Learning graph representations with global structural information,\u201d","volume-title":"Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, CIKM '15","author":"Cao","year":"2015"},{"key":"B19","first-page":"1145","article-title":"\u201cDeep neural networks for learning graph representations,\u201d","volume-title":"Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence","author":"Cao","year":""},{"key":"B20","doi-asserted-by":"publisher","first-page":"35929","DOI":"10.1109\/ACCESS.2020.2975067","article-title":"A comprehensive survey on geometric deep learning","volume":"8","author":"Cao","year":"2020","journal-title":"IEEE Access"},{"key":"B21","article-title":"\u201cLink prediction in schema-rich heterogeneous information network,\u201d","author":"Cao","year":"","journal-title":"Pacific-Asia Conference on Knowledge Discovery and Data Mining"},{"key":"B22","doi-asserted-by":"publisher","first-page":"799","DOI":"10.1109\/TPAMI.2020.3003846","article-title":"ManifoldNet: A deep neural network for manifold-valued data with applications","volume":"44","author":"Chakraborty","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell"},{"key":"B23","doi-asserted-by":"crossref","first-page":"5709","DOI":"10.1109\/CVPR42600.2020.00575","article-title":"\u201cData uncertainty learning in face recognition,\u201d","volume-title":"2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA","author":"Chang","year":"2020"},{"key":"B24","doi-asserted-by":"crossref","DOI":"10.1109\/CVPR.2017.16","article-title":"\u201cPointNet: Deep learning on point sets for 3d classification and segmentation,\u201d","volume-title":"2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Charles","year":"2017"},{"key":"B25","doi-asserted-by":"crossref","DOI":"10.1609\/aaai.v32i1.11849","article-title":"\u201cHarp: Hierarchical representation learning for networks,\u201d","volume-title":"Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence and Thirtieth Innovative Applications of Artificial Intelligence Conference and Eighth AAAI Symposium on Educational Advances in Artificial Intelligence, AAAI'18\/IAAI'18\/EAAI'18","author":"Chen","year":"2018"},{"key":"B26","doi-asserted-by":"publisher","first-page":"4127","DOI":"10.1007\/s11063-022-11032-z","article-title":"Global attention-based graph neural networks for node classification","volume":"55","author":"Chen","year":"2022","journal-title":"Neur. Proc. Lett"},{"key":"B27","first-page":"234","article-title":"\u201cOn the complexity of graph embeddings,\u201d","volume-title":"Lecture Notes in Computer Science","author":"Chen","year":"1993"},{"key":"B28","first-page":"2707","article-title":"\u201cDirected graph embedding,\u201d","volume-title":"Proceedings of the 20th International Joint Conference on Artifical Intelligence, IJCAI'07","author":"Chen","year":"2007"},{"key":"B29","author":"Cheng","year":"2020","journal-title":"Noi gan"},{"key":"B30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.37236\/1142","article-title":"The diameter and laplacian eigenvalues of directed graphs","volume":"13","author":"Chung","year":"2006","journal-title":"Electr. J. Combinator"},{"key":"B31","doi-asserted-by":"publisher","first-page":"e0187301","DOI":"10.1371\/journal.pone.0187301","article-title":"Embedding graphs in lorentzian spacetime","volume":"12","author":"Clough","year":"2017","journal-title":"PLoS ONE"},{"key":"B32","doi-asserted-by":"publisher","first-page":"104640","DOI":"10.1016\/j.chemolab.2022.104640","article-title":"A geometric deep learning model for display and prediction of potential drug-virus interactions against SARS-CoV-2","volume":"229","author":"Das","year":"2022","journal-title":"Chemometrics and Intellig. Lab. Syst"},{"key":"B33","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1186\/s,13321-020-00460-5","article-title":"Molecular representations in AI-driven drug discovery: a review and practical guide","volume":"12","author":"David","year":"2020","journal-title":"J. Cheminform"},{"key":"B34","unstructured":"Readout for Computing Graph Representations; DGL-LifeSci 0.3.1 documentation \u2013 2020"},{"key":"B35","unstructured":"EastR.\n          Introduction to Geometric Quantum Machine Learning | 2023"},{"key":"B36","doi-asserted-by":"publisher","first-page":"100276","DOI":"10.1016\/j.array.2023.100276","article-title":"Semanticgraph2vec: Semantic graph embedding for text representation","volume":"17","author":"Etaiwi","year":"2023","journal-title":"Array"},{"key":"B37","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-017-14411-y","article-title":"GRAFENE: Graphlet-based alignment-free network approach integrates 3d structural and sequence (residue order) data to improve protein structural comparison","volume":"7","author":"Faisal","year":"2017","journal-title":"Sci. Rep"},{"key":"B38","doi-asserted-by":"publisher","first-page":"270","DOI":"10.1016\/j.procs.2021.04.062","article-title":"Word2vec based deep learning network for DNA n4-methylcytosine sites identification","volume":"187","author":"Fang","year":"2021","journal-title":"Procedia Comput. Sci"},{"key":"B39","author":"Fox","year":"2019","journal-title":"How Robust are Graph Neural Networks to Structural Noise"},{"key":"B40","doi-asserted-by":"publisher","first-page":"843","DOI":"10.1007\/s10462-020-09880-z","article-title":"Major advancements in kernel function approximation","volume":"54","author":"Francis","year":"2020","journal-title":"Artif. Intellig. Rev"},{"key":"B41","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1038\/s41592-019-0666-6","article-title":"Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning","volume":"17","author":"Gainza","year":"2019","journal-title":"Nat. Methods"},{"key":"B42","doi-asserted-by":"crossref","unstructured":"GaoK.\n            ZhangJ.\n            ZhouC.\n          Semi-supervised graph embedding for multi-label graph node classification. 2019","DOI":"10.1007\/978-3-030-34223-4_35"},{"key":"B43","doi-asserted-by":"publisher","first-page":"e86028","DOI":"10.1371\/journal.pone.0086028","article-title":"Detecting the community structure and activity patterns of temporal networks: A non-negative tensor factorization approach","volume":"9","author":"Gauvin","year":"2014","journal-title":"PLoS ONE"},{"key":"B44","doi-asserted-by":"publisher","first-page":"4299","DOI":"10.1093\/bioinformatics\/btab439","article-title":"Embeddings of genomic region sets capture rich biological associations in lower dimensions","volume":"37","author":"Gharavi","year":"2021","journal-title":"Bioinformatics"},{"key":"B45","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s13278-023-01058-z","article-title":"A survey on bipartite graphs embedding","volume":"13","author":"Giamphy","year":"2023","journal-title":"Soc. Netw. Analy. Mini"},{"key":"B46","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1016\/j.knosys.2018.03.022","article-title":"Graph embedding techniques, applications, and performance: a survey","volume":"151","author":"Goyal","year":"2018","journal-title":"Knowl.-Based Syst"},{"key":"B47","doi-asserted-by":"crossref","first-page":"855","DOI":"10.1145\/2939672.2939754","article-title":"\u201cNode2vec: Scalable feature learning for networks,\u201d","volume-title":"Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '16","author":"Grover","year":"2016"},{"key":"B48","doi-asserted-by":"publisher","first-page":"1645","DOI":"10.1002\/hlca.19560390623","article-title":"Zusammenhang von Graphentheorie und MO-Theorie von Molekeln mit Systemen konjugierter Bindungen","volume":"39","author":"G\u00fcnthard","year":"1956","journal-title":"Helv. Chim. Acta"},{"key":"B49","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1007\/978-3-030-59728-3_16","article-title":"\u201cDeep graph normalizer: A geometric deep learning approach for estimating connectional brain templates,\u201d","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013MICCAI 2020","author":"Gurbuz","year":"2020"},{"key":"B50","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1186\/s12859-021-04447-3","article-title":"A graph feature auto-encoder for the prediction of unobserved node features on biological networks","volume":"22","author":"Hasibi","year":"2021","journal-title":"BMC Bioinformat"},{"key":"B51","doi-asserted-by":"publisher","first-page":"155","DOI":"10.3389\/frai.2021.155-165","article-title":"A survey of topological machine learning methods","volume":"4","author":"Hensel","year":"2021","journal-title":"Front. Artif. Intellig"},{"key":"B52","doi-asserted-by":"publisher","first-page":"100347","DOI":"10.1016\/j.coisb.2021.05.008","article-title":"Graph representation learning for single-cell biology","volume":"28","author":"Hetzel","year":"2021","journal-title":"Curr. Opini. Syst. Biol"},{"key":"B53","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1016\/j.physrep.2012.03.001","article-title":"Temporal networks","volume":"519","author":"Holme","year":"2012","journal-title":"Phys. Rep"},{"key":"B54","doi-asserted-by":"publisher","first-page":"4132","DOI":"10.1609\/aaai.v34i04.5833","article-title":"An attention-based graph neural network for heterogeneous structural learning","volume":"34","author":"Hong","year":"2020","journal-title":"Proc. Innov. Appl. Artif. Intell. Conf"},{"key":"B55","first-page":"572","author":"Hong","year":"2021"},{"key":"B56","doi-asserted-by":"publisher","first-page":"4371","DOI":"10.1021\/acs.molpharmaceut.7b01144","article-title":"Geometric deep learning autonomously learns chemical features that outperform those engineered by domain experts","volume":"15","author":"Hop","year":"2018","journal-title":"Mol. Pharm"},{"key":"B57","doi-asserted-by":"publisher","first-page":"3609","DOI":"10.1093\/bioinformatics\/btac383","article-title":"Looking at the BiG picture: incorporating bipartite graphs in drug response prediction","volume":"38","author":"Hostallero","year":"2022","journal-title":"Bioinformatics"},{"key":"B58","doi-asserted-by":"crossref","first-page":"731","DOI":"10.1145\/3018661.3018667","article-title":"\u201cLabel informed attributed network embedding,\u201d","volume-title":"Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, WSDM '17","author":"Huang","year":"2017"},{"key":"B59","doi-asserted-by":"publisher","author":"Huang","year":"2021","DOI":"10.1007\/978-3-030-87234-2_51"},{"key":"B60","doi-asserted-by":"publisher","first-page":"102548","DOI":"10.1016\/j.sbi.2023.102548","article-title":"Structure-based drug design with geometric deep learning","volume":"79","author":"Isert","year":"2023","journal-title":"Curr. Opin. Struct. Biol"},{"key":"B61","doi-asserted-by":"publisher","first-page":"7400","DOI":"10.1109\/TNNLS.2021.3084957","article-title":"Temporal network embedding for link prediction via VAE joint attention mechanism","volume":"33","author":"Jiao","year":"2022","journal-title":"IEEE Trans. Neural Netw. Learn. Syst"},{"key":"B62","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/00029890.1966.11970915","article-title":"Can one hear the shape of a drum?","volume":"73","author":"Kac","year":"1966","journal-title":"Am. Mathematical Monthly"},{"key":"B63","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1093\/comnet\/cnz043","article-title":"An unsupervised framework for comparing graph embeddings","volume":"8","author":"Kami\u0144ski","year":"2019","journal-title":"J. Complex Netw"},{"key":"B64","doi-asserted-by":"crossref","unstructured":"KarpukhinI.\n            DerekaS.\n            KolesnikovS.\n          GitHub - tinkoff-ai\/probabilistic-embeddings: \u201cProbabilistic Embeddings Revisited\u201d Paper Official Repository \u20142022","DOI":"10.1007\/s00371-023-03087-3"},{"key":"B65","unstructured":"Genome alignment with graph data structures: a comparison99\n            KehrB.\n            TrappeK.\n            HoltgreweM.\n            ReinertK.\n          10.1186\/1471-2105-15-9924712884BMC Bioinformat152014"},{"key":"B66","first-page":"395","article-title":"\u201cNode representation learning for directed graphs,\u201d","volume-title":"Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2019, Proceedings, Vol. 11906","author":"Khosla","year":"2020"},{"key":"B67","doi-asserted-by":"publisher","first-page":"4743","DOI":"10.1021\/acs.jctc.3c00031","article-title":"Geometric deep learning for molecular crystal structure prediction","volume":"19","author":"Kilgour","year":"2023","journal-title":"J. Chem. Theory Comput"},{"key":"B68","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1186\/s12859-018-2200-8","article-title":"Relation extraction for biological pathway construction using node2vec","volume":"19","author":"Kim","year":"2018","journal-title":"BMC Bioinformat"},{"key":"B69","doi-asserted-by":"publisher","first-page":"604","DOI":"10.1145\/324133.324140","article-title":"Authoritative sources in a hyperlinked environment","volume":"46","author":"Kleinberg","year":"1999","journal-title":"J. ACM"},{"key":"B70","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1007\/978-1-4419-9863-7_992","article-title":"\u201cGraph alignment, protein interaction networks,\u201d","volume-title":"Encyclopedia of Systems Biology","author":"Kol\u00e1\u0159","year":"2013"},{"key":"B71","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1145\/2824443","article-title":"Deltacon: Principled massive-graph similarity function with attribution","volume":"10","author":"Koutra","year":"2016","journal-title":"ACM Trans. Knowl. Discov. Data"},{"key":"B72","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/s41109-019-0195-3","article-title":"A survey on graph kernels","volume":"5","author":"Kriege","year":"2020","journal-title":"Appl. Netw. Sci"},{"key":"B73","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1103\/PhysRevLett.116.208302","article-title":"Clustering implies geometry in networks","volume":"116","author":"Krioukov","year":"2016","journal-title":"Phys. Rev. Lett"},{"key":"B74","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1103\/PhysRevE.82.036106","article-title":"Hyperbolic geometry of complex networks","volume":"82","author":"Krioukov","year":"2010","journal-title":"Physical Review E"},{"key":"B75","first-page":"22058","article-title":"\u201cUltrahyperbolic neural networks,\u201d","author":"Law","year":"2021","journal-title":"Advances in Neural Information Processing Systems"},{"key":"B76","unstructured":"\u201cSpacetime representation learning,\u201d\n            LawM. T.\n            LucasJ.\n          Proceedings of The Eleventh Inter International Conference on Learning Representations, ICLR2023"},{"key":"B77","article-title":"\u201cUltrahyperbolic representation learning,\u201d","volume-title":"Proceedings of the 34th International Conference on Neural Information Processing Systems, NIPS'20","author":"Law","year":"2020"},{"key":"B78","doi-asserted-by":"crossref","DOI":"10.1109\/BIBM55620.2022.9995274","article-title":"\u201cChecking for non-euclidean latent geometry of biological networks,\u201d","volume-title":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","author":"Lecca","year":"2022"},{"key":"B79","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1145\/382979.383041","article-title":"SALSA","volume":"19","author":"Lempel","year":"2001","journal-title":"ACM Trans. Informat. Syst"},{"key":"B80","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3473338","article-title":"SPEX: A generic framework for enhancing neural social recommendation","volume":"40","author":"Li","year":"","journal-title":"ACM Trans. Informat. Syst"},{"key":"B81","first-page":"1004","article-title":"\u201cDiscriminative deep random walk for network classification,\u201d","volume-title":"Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","author":"Li","year":""},{"key":"B82","first-page":"1004","article-title":"\u201cDiscriminative deep random walk for network classification,\u201d","volume-title":"Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","author":"Li","year":""},{"key":"B83","first-page":"269","article-title":"\u201cObject detection in omnidirectional images based on spherical cnn,\u201d","author":"Li","year":"","journal-title":"2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)"},{"key":"B84","doi-asserted-by":"publisher","first-page":"2257","DOI":"10.1109\/TKDE.2018.2819980","article-title":"Attributed social network embedding","volume":"30","author":"Liao","year":"2018","journal-title":"IEEE Trans. Knowl. Data Eng"},{"key":"B85","doi-asserted-by":"crossref","DOI":"10.1145\/3437963.3441741","article-title":"\u201cLearning dynamic embeddings for temporal knowledge graphs,\u201d","volume-title":"Proceedings of the 14th ACM International Conference on Web Search and Data Mining","author":"Liao","year":"2021"},{"key":"B86","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1007\/3-540-58325-4_168","article-title":"\u201cHardness of approximating graph transformation problem,\u201d","volume-title":"Algorithms and Computation","author":"Lin","year":"1994"},{"key":"B87","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2020\/7953758","article-title":"Semisupervised community preserving network embedding with pairwise constraints","volume":"2020","author":"Liu","year":"2020","journal-title":"Complexity"},{"key":"B88","doi-asserted-by":"publisher","first-page":"16069","DOI":"10.1007\/s10489-021-03102-x","article-title":"Embedding temporal networks inductively via mining neighborhood and community influences","volume":"52","author":"Liu","year":"2022","journal-title":"Appl. Intellig"},{"key":"B89","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1038\/s41598-018-21532-5","article-title":"Cut based method for comparing complex networks","volume":"8","author":"Liu","year":"2018","journal-title":"Sci. Rep"},{"key":"B90","doi-asserted-by":"publisher","DOI":"10.3389\/fgene.2023.1136672","article-title":"Predicting lncRNA-protein interactions with bipartite graph embedding and deep graph neural networks","author":"Ma","year":"2023","journal-title":"Front. Genet"},{"key":"B91","article-title":"Topology and geometry of data manifold in deep learning","author":"Magai","year":"2022","journal-title":"arXiv preprint arXiv:2204.08624"},{"key":"B92","doi-asserted-by":"publisher","DOI":"10.7717\/peerj-cs.858","article-title":"Temporal network embedding framework with causal anonymous walks representations","author":"Makarov","year":"2022","journal-title":"PeerJ. Comp. Sci"},{"key":"B93","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1186\/s12859-020-3518-6","article-title":"Drug-target interaction prediction using semi-bipartite graph model and deep learning","volume":"21","author":"Manoochehri","year":"2020","journal-title":"BMC Bioinformat"},{"key":"B94","doi-asserted-by":"crossref","DOI":"10.1137\/1.9781611972771.13","article-title":"\u201cClustering by weighted cuts in directed graphs,\u201d","volume-title":"Proceedings of the 2007 SIAM International Conference on Data Mining","author":"Meil\u0103","year":"2007"},{"key":"B95","doi-asserted-by":"publisher","first-page":"101721","DOI":"10.1109\/ACCESS.2020.2996495","article-title":"Uncertainty-based rejection wrappers for black-box classifiers","volume":"8","author":"Mena","year":"2020","journal-title":"IEEE Access"},{"key":"B96","unstructured":"MisraA.\n          Capsule Networks: The New Deep Learning Network2019"},{"key":"B97","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1007\/s42452-019-1044-9","article-title":"Network representation learning: models, methods and applications","volume":"1","author":"Mohan","year":"2019","journal-title":"SN Appl. Sci"},{"key":"B98","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1007\/s40747-021-00332-x","article-title":"Temporal network embedding using graph attention network","volume":"8","author":"Mohan","year":"2021","journal-title":"Complex Intellig. Syst"},{"key":"B99","doi-asserted-by":"publisher","first-page":"381","DOI":"10.3389\/fgene.2019.00381","article-title":"To embed or not: network embedding as a paradigm in computational biology","volume":"10","author":"Nelson","year":"2019","journal-title":"Front. Genet"},{"key":"B100","doi-asserted-by":"crossref","DOI":"10.1145\/3184558.3191526","article-title":"\u201cContinuous-time dynamic network embeddings,\u201d","author":"Nguyen","year":"2018","journal-title":"Companion of the The Web Conference 2018 on The Web Conference 2018"},{"key":"B101","unstructured":"\u201cRobust graph embedding with noisy link weights,\u201d664673\n            OkunoA.\n            ShimodairaH.\n          Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics, Vol. 892019"},{"key":"B102","doi-asserted-by":"publisher","first-page":"2697","DOI":"10.1093\/bioinformatics\/btv170","article-title":"Proper evaluation of alignment-free network comparison methods","volume":"31","author":"\u00d6mer Nebil Yavero\u011flu Milenkovi\u0107","year":"2015","journal-title":"Bioinformatics"},{"key":"B103","doi-asserted-by":"crossref","first-page":"1105","DOI":"10.1145\/2939672.2939751","article-title":"\u201cAsymmetric transitivity preserving graph embedding,\u201d","volume-title":"Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '16","author":"Ou","year":"2016"},{"key":"B104","doi-asserted-by":"publisher","first-page":"689","DOI":"10.1016\/j.drudis.2020.01.020","article-title":"Exploring chemical space using natural language processing methodologies for drug discovery","volume":"25","author":"\u00d6zt\u00fcrk","year":"2020","journal-title":"Drug Discov. Today"},{"key":"B105","doi-asserted-by":"publisher","first-page":"2475","DOI":"10.1109\/TCYB.2019.2932096","article-title":"Learning graph embedding with adversarial training methods","volume":"50","author":"Pan","year":"2020","journal-title":"IEEE Trans. Cybern"},{"key":"B106","first-page":"1895","article-title":"\u201cTri-party deep network representation,\u201d","volume-title":"Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI'16","author":"Pan","year":"2016"},{"key":"B107","doi-asserted-by":"crossref","DOI":"10.1145\/3152494.3152512","article-title":"\u201cSTwalk,\u201d","volume-title":"Proceedings of the ACM India Joint International Conference on Data Science and Management of Data","author":"Pandhre","year":"2018"},{"key":"B108","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1103\/PhysRevE.100.052313","article-title":"Latent geometry and dynamics of proximity networks","volume":"100","author":"Papadopoulos","year":"2019","journal-title":"Physical Rev. E"},{"key":"B109","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1093\/gigascience\/giy014","article-title":"Bipartite graphs in systems biology and medicine: a survey of methods and applications","volume":"7","author":"Pavlopoulos","year":"2018","journal-title":"Gigascience"},{"key":"B110","doi-asserted-by":"crossref","DOI":"10.1145\/2623330.2623732","article-title":"\u201cDeepWalk,\u201d","volume-title":"Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","author":"Perozzi","year":"2014"},{"key":"B111","article-title":"Walklets: Multiscale graph embeddings for interpretable network classification","author":"Perozzi","year":"2016","journal-title":"ArXiv"},{"key":"B112","article-title":"\u201cDirected graph embedding: an algorithm based on continuous limits of laplacian-type operators,\u201d","author":"Perrault-joncas","year":"2011","journal-title":"Advances in Neural Information Processing Systems"},{"key":"B113","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1038\/s42256-022-00595-0","article-title":"Geometric deep learning reveals the spatiotemporal features of microscopic motion","volume":"5","author":"Pineda","year":"2023","journal-title":"Nat. Mach. Intellig"},{"key":"B114","doi-asserted-by":"crossref","DOI":"10.18653\/v1\/D17-1184","article-title":"\u201cSparsity and noise: Where knowledge graph embeddings fall short,\u201d","volume-title":"Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing","author":"Pujara","year":"2017"},{"key":"B115","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1145\/3159652.3159706","article-title":"\u201cNetwork embedding as matrix factorization: Unifying deepwalk, line, pte, and node2vec,\u201d","volume-title":"Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, WSDM '18","author":"Qiu","year":"2018"},{"key":"B116","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1016\/j.copbio.2019.12.002","article-title":"Network inference in systems biology: recent developments, challenges, and applications","volume":"63","author":"Saint-Antoine","year":"2020","journal-title":"Curr. Opin. Biotechnol"},{"key":"B117","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TNNLS.2022.3177742","article-title":"Spectral graph convolutional neural networks in the context of regularization theory","volume":"2022","author":"Salim","year":"2022","journal-title":"IEEE Trans. Neural Netw. Learn. Syst"},{"key":"B118","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1140\/epjds\/s13688-021-00277-8","article-title":"Predicting partially observed processes on temporal networks by Dynamics-Aware Node Embeddings (DyANE)","volume":"10","author":"Sato","year":"2021","journal-title":"EPJ Data Sci"},{"key":"B119","unstructured":"SaxenaT.\n            XuD.\n          Graph Alignment-Based Protein Comparison2021"},{"key":"B120","doi-asserted-by":"publisher","first-page":"5383","DOI":"10.1021\/acs.jcim.2c00832","article-title":"Classification of protein-binding sites using a spherical convolutional neural network","volume":"62","author":"Scott","year":"2022","journal-title":"J. Chem. Inf. Model"},{"key":"B121","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1007\/s42979-021-00792-5","article-title":"Redefining the graph edit distance","volume":"2","author":"Serratosa","year":"2021","journal-title":"SN Comp. Sci"},{"key":"B122","doi-asserted-by":"publisher","first-page":"1556","DOI":"10.1109\/TCYB.2018.2871503","article-title":"Deep network embedding for graph representation learning in signed networks","volume":"50","author":"Shen","year":"2020","journal-title":"IEEE Trans. Cybern"},{"key":"B123","doi-asserted-by":"publisher","first-page":"3682","DOI":"10.1109\/TNNLS.2019.2945869","article-title":"Mlne: Multi-label network embedding","volume":"31","author":"Shi","year":"2020","journal-title":"IEEE Trans. Neural Netw. Learn. Syst"},{"key":"B124","article-title":"\u201cDirected graph embeddings in pseudo-riemannian manifolds,\u201d","volume-title":"International Conference on Machine Learning","author":"Sim","year":"2021"},{"key":"B125","doi-asserted-by":"crossref","DOI":"10.24963\/ijcai.2019\/640","article-title":"\u201cNode embedding over temporal graphs,\u201d","volume-title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","author":"Singer","year":"2019"},{"key":"B126","unstructured":"SivakumarD.\n          Introduction to Geometric Deep Learning - 2023"},{"key":"B127","doi-asserted-by":"publisher","first-page":"115908","DOI":"10.1016\/j.eswa.2021.115908","article-title":"Grar: a novel framework for graph alignment based on relativity concept","volume":"187","author":"Soltanshahi","year":"2022","journal-title":"Expert Syst. Appl"},{"key":"B128","doi-asserted-by":"publisher","first-page":"1220","DOI":"10.1109\/TBDATA.2020.3034201","article-title":"Domain adaptive network embedding","volume":"8","author":"Song","year":"2022","journal-title":"IEEE Trans. Big Data"},{"key":"B129","doi-asserted-by":"publisher","first-page":"8371","DOI":"10.1109\/TPAMI.2021.3113612","article-title":"Learning spherical convolution for 360 recognition","volume":"44","author":"Su","year":"2021","journal-title":"IEEE Trans. Pattern Analy. Mach. Intellig"},{"key":"B130","doi-asserted-by":"crossref","DOI":"10.1145\/2783258.2783307","article-title":"\u201cPTE,\u201d","volume-title":"Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","author":"Tang","year":""},{"key":"B131","article-title":"\u201cLine: Large-scale information network embedding,\u201d","volume-title":"Proceedings of the 24th International Conference on World Wide Web","author":"Tang","year":""},{"key":"B132","author":"Tang","year":""},{"key":"B133","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-019-53708-y","article-title":"Comparing methods for comparing networks","volume":"9","author":"Tantardini","year":"2019","journal-title":"Sci. Rep"},{"key":"B134","unstructured":"TongF.\n          2019"},{"key":"B135","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-020-63221-2","article-title":"weg2vec: Event embedding for temporal networks","volume":"10","author":"Torricelli","year":"2020","journal-title":"Sci. Rep"},{"key":"B136","doi-asserted-by":"publisher","first-page":"1047","DOI":"10.1126\/science.abe5650","article-title":"Geometric deep learning of RNA structure","volume":"373","author":"Townshend","year":"2021","journal-title":"Science"},{"key":"B137","article-title":"\u201cNetLSD,\u201d","author":"Tsitsulin","year":"2018","journal-title":"Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery"},{"key":"B138","doi-asserted-by":"crossref","first-page":"522","DOI":"10.1007\/978-3-030-44584-3_41","article-title":"\u201cComparing the preservation of network properties by graph embeddings,\u201d","author":"Vaudaine","year":"2020","journal-title":"Lecture Notes in Computer Science"},{"key":"B139","unstructured":"\u201cGraph attention networks,\u201d\n            Veli\u010dkovi\u0107P.\n            CucurullG.\n            CasanovaA.\n            RomeroA.\n            Li\u00f2P.\n            BengioY.\n          International Conference on Learning Representations2018"},{"key":"B140","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1007\/s11222-007-9033-z","article-title":"A tutorial on spectral clustering","volume":"17","author":"von Luxburg","year":"2007","journal-title":"Stat. Comput"},{"key":"B141","doi-asserted-by":"publisher","first-page":"4563","DOI":"10.3390\/math10234563","article-title":"Attributed graph embedding based on attention with cluster","volume":"10","author":"Wang","year":"2022","journal-title":"Mathematics"},{"key":"B142","doi-asserted-by":"crossref","DOI":"10.1145\/3132847.3132967","article-title":"\u201cMGAE,\u201d","volume-title":"Proceedings of the 2017 ACM on Conference on Information and Knowledge Management","author":"Wang","year":"2017"},{"key":"B143","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3391298","article-title":"Edge2vec","volume":"14","author":"Wang","year":"2020","journal-title":"ACM Trans. Knowl. Discov. Data"},{"key":"B144","doi-asserted-by":"crossref","DOI":"10.1145\/2939672.2939753","article-title":"\u201cStructural deep network embedding,\u201d","volume-title":"Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","author":"Wang","year":"2016"},{"key":"B145","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1021\/ci00057a005","article-title":"SMILES, a chemical language and information system. 1. introduction to methodology and encoding rules","volume":"28","author":"Weininger","year":"1988","journal-title":"J. Chem. Inform. Comp. Sci"},{"key":"B146","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1007\/978-3-030-87586-2_11","article-title":"\u201cGeometric deep learning of the human connectome project multimodal cortical parcellation,\u201d","author":"Williams","year":"2021","journal-title":"Machine Learning in Clinical Neuroimaging"},{"key":"B147","doi-asserted-by":"publisher","first-page":"e0228728","DOI":"10.1371\/journal.pone.0228728","article-title":"Metrics for graph comparison: a practitioner's guide","volume":"15","author":"Wills","year":"2020","journal-title":"PLoS ONE"},{"key":"B148","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-022-22313-x","article-title":"A multicohort geometric deep learning study of age dependent cortical and subcortical morphologic interactions for fluid intelligence prediction","volume":"12","author":"Wu","year":"2022","journal-title":"Sci. Rep"},{"key":"B149","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1011207","article-title":"Knowledge graph embedding for profiling the interaction between transcription factors and their target genes","author":"Wu","year":"2023","journal-title":"PLoS Comput. Biol"},{"key":"B150","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TKDE.2023.3288002","article-title":"Gnn cleaner: Label cleaner for graph structured data","volume":"2023","author":"Xia","year":"2023","journal-title":"IEEE Trans. Know. Data Eng"},{"key":"B151","doi-asserted-by":"publisher","first-page":"107835","DOI":"10.1016\/j.compeleceng.2022.107835","article-title":"Structural\u2013temporal embedding of large-scale dynamic networks with parallel implementation","volume":"100","author":"Xie","year":"2022","journal-title":"Comp. Electrical Eng"},{"key":"B152","doi-asserted-by":"publisher","first-page":"8749","DOI":"10.1021\/acs.jmedchem.9b00959","article-title":"Pushing the boundaries of molecular representation for drug discovery with the graph attention mechanism","volume":"63","author":"Xiong","year":"2019","journal-title":"J. Med. Chem"},{"key":"B153","doi-asserted-by":"publisher","first-page":"825","DOI":"10.1137\/20M1386062","article-title":"Understanding graph embedding methods and their applications","volume":"63","author":"Xu","year":"2021","journal-title":"SIAM Review"},{"key":"B154","doi-asserted-by":"publisher","first-page":"122050","DOI":"10.1016\/j.physa.2019.122050","article-title":"Modularized tri-factor nonnegative matrix factorization for community detection enhancement","volume":"533","author":"Yan","year":"2019","journal-title":"Physica A"},{"key":"B155","doi-asserted-by":"crossref","DOI":"10.24963\/ijcai.2017\/544","article-title":"\u201cFast network embedding enhancement via high order proximity approximation,\u201d","volume-title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","author":"Yang","year":"2017"},{"key":"B156","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12859-020-03646-8","article-title":"Graph-based prediction of protein-protein interactions with attributed signed graph embedding","volume":"21","author":"Yang","year":"2020","journal-title":"BMC Bioinformat"},{"key":"B157","doi-asserted-by":"crossref","first-page":"1476","DOI":"10.1109\/ICDM.2018.8626170","article-title":"\u201cBinarized attributed network embedding,\u201d","author":"Yang","year":"2018","journal-title":"2018 IEEE International Conference on Data Mining (ICDM)"},{"key":"B158","doi-asserted-by":"publisher","first-page":"11489","DOI":"10.1109\/TKDE.2022.3232398","article-title":"Hyperbolic temporal network embedding","volume":"35","author":"Yang","year":"","journal-title":"IEEE Trans. Knowl. Data Eng"},{"key":"B159","doi-asserted-by":"crossref","DOI":"10.1145\/3514221.3517838","article-title":"\u201cScalable and effective bipartite network embedding,\u201d","volume-title":"Proceedings of the 2022 International Conference on Management of Data","author":"Yang","year":""},{"key":"B160","article-title":"\u201cMultilabel classification based on graph neural networks,\u201d","volume-title":"Artificial Intelligence","author":"Ye","year":"2022"},{"key":"B161","first-page":"4805","article-title":"\u201cHierarchical graph representation learning with differentiable pooling,\u201d","volume-title":"Proceedings of the 32nd International Conference on Neural Information Processing Systems, NIPS'18","author":"Ying","year":"2018"},{"key":"B162","doi-asserted-by":"publisher","first-page":"104098","DOI":"10.1016\/j.jbi.2022.104098","article-title":"idse-HE: Hybrid embedding graph neural network for drug side effects prediction","volume":"131","author":"Yu","year":"2022","journal-title":"J. Biomed. Inform"},{"key":"B163","doi-asserted-by":"publisher","first-page":"1241","DOI":"10.1093\/bioinformatics\/btz718","article-title":"Graph embedding on biomedical networks: methods, applications and evaluations","volume":"36","author":"Yue","year":"2019","journal-title":"Bioinformatics"},{"key":"B164","doi-asserted-by":"crossref","DOI":"10.24963\/ijcai.2017\/472","article-title":"\u201cUser profile preserving social network embedding,\u201d","volume-title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","author":"Zhang","year":"2017"},{"key":"B165","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1109\/TBDATA.2018.2850013","article-title":"Network representation learning: A survey","volume":"6","author":"Zhang","year":"2020","journal-title":"IEEE Trans. Big Data"},{"key":"B166","doi-asserted-by":"crossref","first-page":"2464","DOI":"10.1109\/CVPR.2012.6247961","article-title":"\u201cRobust non-negative graph embedding: Towards noisy data, unreliable graphs, and noisy labels,\u201d","author":"Zhang","year":"2012","journal-title":"2012 IEEE Conference on Computer Vision and Pattern Recognition"},{"key":"B167","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40649-019-0069-y","article-title":"Graph convolutional networks: a comprehensive review","volume":"6","author":"Zhang","year":"","journal-title":"Computat. Soc. Netw"},{"key":"B168","first-page":"27003","article-title":"Magnet: A neural network for directed graphs","volume":"34","author":"Zhang","year":"","journal-title":"Adv. Neural Inf. Process. Syst"},{"key":"B169","doi-asserted-by":"publisher","first-page":"690049","DOI":"10.3389\/fgene.2021.690049","article-title":"Graph neural networks and their current applications in bioinformatics","volume":"12","author":"Zhang","year":"","journal-title":"Front. Genet"},{"key":"B170","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41597-019-0055-0","article-title":"BioWordVec, improving biomedical word embeddings with subword information and MeSH","volume":"6","author":"Zhang","year":"","journal-title":"Scient. Data"},{"key":"B171","doi-asserted-by":"publisher","first-page":"044315","DOI":"10.1103\/PhysRevE.104.044315","article-title":"Systematic comparison of graph embedding methods in practical tasks","volume":"104","author":"Zhang","year":"","journal-title":"Phys. Rev. E"},{"key":"B172","doi-asserted-by":"crossref","first-page":"2778","DOI":"10.1145\/3219819.3219969","article-title":"\u201cArbitrary-order proximity preserved network embedding,\u201d","author":"Zhang","year":"2018","journal-title":"Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery"},{"key":"B173","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1093\/bib\/bbac384","article-title":"A geometric deep learning framework for drug repositioning over heterogeneous information networks","volume":"23","author":"Zhao","year":"2022","journal-title":"Brief. Bioinformatics"},{"key":"B174","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s13278-020-00714-y","article-title":"ShortWalk: an approach to network embedding on directed graphs","volume":"11","author":"Zhao","year":"2021","journal-title":"Soc. Netw. Analy. Mining"},{"key":"B175","first-page":"2641","article-title":"Hierarchical representation learning for attributed networks","volume":"35","author":"Zhao","year":"2023","journal-title":"IEEE Trans. Knowl. Data Eng"},{"key":"B176","first-page":"115","article-title":"\u201cA computational bipartite graph-based drug repurposing method,\u201d","volume-title":"Methods in Molecular Biology","author":"Zheng","year":"2018"},{"key":"B177","article-title":"\u201cSemi-supervised learning on directed graphs,\u201d","author":"Zhou","year":"2004","journal-title":"Advances in Neural Information Processing Systems"},{"key":"B178","article-title":"\u201cLearning from labeled and unlabeled data on a directed graph,\u201d","author":"Zhou","year":"2005","journal-title":"Proceedings of the 22nd international conference on Machine learning"},{"key":"B179","doi-asserted-by":"publisher","first-page":"1182","DOI":"10.3389\/fgene.2019.01182","article-title":"Graph embedding deep learning guides microbial biomarkers' identification","volume":"10","author":"Zhu","year":"2019","journal-title":"Front. Genet"}],"container-title":["Frontiers in Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/frai.2023.1256352\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,16]],"date-time":"2023-11-16T14:01:45Z","timestamp":1700143305000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/frai.2023.1256352\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,16]]},"references-count":179,"alternative-id":["10.3389\/frai.2023.1256352"],"URL":"https:\/\/doi.org\/10.3389\/frai.2023.1256352","relation":{},"ISSN":["2624-8212"],"issn-type":[{"value":"2624-8212","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,16]]},"article-number":"1256352"}}