node2vec: Scalable Feature Learning for Networks

Glove: Global Vectors for Word Representation

Pennington, J., Socher, R., & Manning, C. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1162 · Cited by 34,071

Efficient Estimation of Word Representations in Vector Space

Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1301.3781 · Cited by 26,568

Nonlinear Dimensionality Reduction by Locally Linear Embedding

Roweis, S. T., & Saul, L. K. (2000) · Science · doi:10.1126/science.290.5500.2323 · Cited by 14,965

A Global Geometric Framework for Nonlinear Dimensionality Reduction

Tenenbaum, J. B., Silva, V. D., & Langford, J. C. (2000) · Science · doi:10.1126/science.290.5500.2319 · Cited by 13,585

Representation Learning: A Review and New Perspectives

Bengio, Y., Courville, A., & Vincent, P. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.50 · Cited by 13,001

Community detection in graphs

Fortunato, S. (2010) · Physics Reports · doi:10.1016/j.physrep.2009.11.002 · Cited by 11,293

DeepWalk: online learning of social representations

Perozzi, B., Al-Rfou, R., & Skiena, S. (2014) · Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2623330.2623732 · Cited by 10,123

Molecular signatures database (MSigDB) 3.0

Liberzon, A., Subramanian, A., Pinchback, R., Thorvaldsdóttir, H., Tamayo, P., & Mesirov, J. P. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr260 · Cited by 7,111

LINE: Large-scale Information Network Embedding

Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., & Mei, Q. (2015) · Proceedings of the 24th International Conference on World Wide Web · doi:10.1145/2736277.2741093 · Cited by 5,416

Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering

Belkin, M., & Niyogi, P. (2002) · The MIT Press eBooks · doi:10.7551/mitpress/1120.003.0080 · Cited by 4,534

Inference Attacks Against Graph Neural Networks

Zhang, Z., Chen, M., Michael Backes, A., Shen, Y. C., & Zhang, Y. (2021) · VU Research Portal · doi:10.22028/d291-47486 · Cited by 59

Graph representation learning for single-cell biology

Hetzel, L., Fischer, D. S., Günnemann, S., & Theis, F. J. (2021) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2021.05.008 · Cited by 55

Genome-wide prediction of pathogenic gain- and loss-of-function variants from ensemble learning of a diverse feature set

Stein, D., Kars, M. E., Wu, Y., Bayrak, Ç. S., Stenson, P. D., Cooper, D. N., Schlessinger, A., & Itan, Y. (2023) · Genome Medicine · doi:10.1186/s13073-023-01261-9 · Cited by 53

Deep learning methods for protein function prediction

Boadu, F., Lee, A., & Cheng, J. (2025) · PROTEOMICS · doi:10.1002/pmic.202300471 · Cited by 53

Biomedical data, computational methods and tools for evaluating disease–disease associations

Xiang, J., Zhang, J., Zhao, Y., Wu, F.-X., & Li, M. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac006 · Cited by 49

HeteroMed: Heterogeneous Information Network for Medical Diagnosis

Hosseini, A., Chen, T., Wu, W., Sun, Y., & Sarrafzadeh, M. (2018) · Proceedings of the 27th ACM International Conference on Information and Knowledge Management · doi:10.1145/3269206.3271805 · Cited by 47

Graph neural networks for single-cell omics data: a review of approaches and applications

Li, S., Hua, H., & Chen, S. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf109 · Cited by 43

Motif-Backdoor: Rethinking the Backdoor Attack on Graph Neural Networks via Motifs

Zheng, H., Xiong, H., Chen, J., Ma, H., & Huang, G. (2024) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2023.3267094 · Cited by 41

RareBench: Can LLMs Serve as Rare Diseases Specialists?

Chen, X., Mao, X., Guo, Q., Wang, L., Zhang, S., & Chen, T. (2024) · Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining · doi:10.1145/3637528.3671576 · Cited by 41

Harmonized representation learning on dynamic EHR graphs

Lee, D., Jiang, X., & Yu, H. (2020) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2020.103426 · Cited by 34

Radiolysis Generates a Complex Organosynthetic Chemical Network

Adam, Z., Fahrenbach, A. C., Jacobson, S. M., Kacar, B., & Zubarev, D. Y. (2019) · doi:10.26434/chemrxiv.10148684 · Cited by 34

Deep learning prediction of chemical-induced dose-dependent and context-specific multiplex phenotype responses and its application to personalized alzheimer’s disease drug repurposing

Wu, Y., Liu, Q., Qiu, Y., & Xie, L. (2022) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1010367 · Cited by 34

An ontology-based knowledge graph for representing interactions involving RNA molecules

Cavalleri, E., Cabri, A., Soto-Gomez, M., Bonfitto, S., Perlasca, P., Gliozzo, J., Callahan, T. J., Reese, J., Robinson, P. N., Casiraghi, E., et al. (2024) · Scientific Data · doi:10.1038/s41597-024-03673-7 · Cited by 33

Disease Module Identification Based on Representation Learning of Complex Networks Integrated From GWAS, eQTL Summaries, and Human Interactome

Wang, T., Peng, Q., Liu, B., Liu, Y., & Wang, Y. (2020) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2020.00418 · Cited by 27

Deep learning approaches for similarity computation: A survey

Yang, P., Wang, H., Yang, J., Qian, Z., Zhang, Y., & Lin, X. (2024) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2024.3422484 · Cited by 28

Formatting biological big data for modern machine learning in drug discovery

Duran‐Frigola, M., Fernández‐Torras, A., Bertoni, M., & Aloy, P. (2019) · WIREs Computational Molecular Science · doi:10.1002/wcms.1408 · Cited by 25

Aceso: PICO-Guided Evidence Summarization on Medical Literature.

Zhang, X., Geng, P., Zhang, T., Lu, Q., Gao, P., & Mei, J. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.2984704 · Cited by 25

Denoising Protein–Protein interaction network via variational graph auto-encoder for protein complex detection

Yao, H., Guan, J., & Liu, T. (2020) · Journal of Bioinformatics and Computational Biology · doi:10.1142/s0219720020400107 · Cited by 23

Learning Invariant Representations of Social Media Users

Andrews, N., & Bishop, M. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1178 · Cited by 19

Healthwalks: sensing fine-grained individual health condition via mobility data

Lin, Z., Lyu, S., Cao, H., Xu, F., Wei, Y., Samet, H., & Li, Y. (2020) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3432229 · Cited by 18

TE-G-SAGE: Explainable Edge-Aware Graph Neural Networks for Network Intrusion Detection

Luša, R., Pintar, D., & Vranić, M. (2025) · Modelling · doi:10.3390/modelling6040165 · Cited by 15

Clinical trial recommendations using Semantics-Based inductive inference and knowledge graph embeddings

Devarakonda, M. V., Mohanty, S., Sunkishala, R. R., Mallampalli, N., & Liu, X. (2024) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2024.104627 · Cited by 9

Constrained-optimization Approach Delivers Superior Classical Performance for Graph Partitioning via Quantum-ready Method

Chukwu, U., Dridi, R., Berwald, J., Booth, M., Dawson, J., Le, D., Wainger, M., & Reinhardt, S. P. (2020) · 2020 IEEE High Performance Extreme Computing Conference (HPEC) · doi:10.1109/hpec43674.2020.9286230 · Cited by 9

Pseudo2GO: A Graph-Based Deep Learning Method for Pseudogene Function Prediction by Borrowing Information From Coding Genes

Fan, K., & Zhang, Y. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.00807 · Cited by 7

A Novel Framework for Enhancing Decision-Making in Autonomous Cyber Defense Through Graph Embedding

Wang, Z., Wang, Y., Xiong, X., Ren, Q., & Huang, J. (2025) · Entropy · doi:10.3390/e27060622 · Cited by 7

The EFFECT benchmark suite: measuring cancer sensitivity prediction performance - without the bias

Szalai, B., Gáspár, I., Kaszás, V., Mérő, L., Sztilkovics, M., & Szalay, K. Z. (2023) · doi:10.1101/2023.10.02.560281 · Cited by 5

Data Organisation for Efficient Pattern Retrieval: Indexing, Storage, and Access Structures

Koukaras, P., & Tjortjis, C. (2025) · Big Data and Cognitive Computing · doi:10.3390/bdcc9100258 · Cited by 3

Link Prediction for Completing Graphical Software Models Using Neural Networks

Leblebici, O., Tuglular, T., & Belli, F. (2023) · IEEE Access · doi:10.1109/access.2023.3323591 · Cited by 1

Untitled work

(2018) · doi:10.4230/lipics.cp.2025.27

Adversarial Unsupervised Representation Learning for Activity Time-Series

Aggarwal, K., Joty, S., Fernandez-Luque, L., & Srivastava, J. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.3301834

BalNode2Vec: Balanced Random Walk based Versatile Feature Learning for Networks

Salamat, A., Luo, X., & Jafari, A. (2020) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn48605.2020.9206737 · Cited by 6

BiasedWalk: Biased Sampling for Representation Learning on Graphs

Nguyen, D., & Malliaros, F. D. (2018) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2018.8621872 · Cited by 20

motif2vec: Motif Aware Node Representation Learning for Heterogeneous Networks

Dareddy, M. R., Das, M., & Yang, H. (2019) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata47090.2019.9005670 · Cited by 25

Core2Vec: A Core-Preserving Feature Learning Framework for Networks

Sarkar, S., Bhagwat, A., & Mukherjee, A. (2018) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1109/asonam.2018.8508693 · Cited by 8

Network2Vec: Learning Node Representation Based on Space Mapping in Networks

Huang, Z., Wang, Z., Zhang, R., Zhao, Y., Xie, X. S., & Mehrotra, S. (2019) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2019.00109 · Cited by 1