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

AccPar: Tensor Partitioning for Heterogeneous Deep Learning Accelerators

Song, L., Chen, F., Zhuo, Y., Qian, X., Li, H. H., & Chen, Y. (2020) · IEEE International Symposium on High-Performance Computer Architecture (HPCA) · doi:10.1109/hpca47549.2020.00036 · Cited by 67

DepthLGP: Learning Embeddings of Out-of-Sample Nodes in Dynamic Networks

Ma, J., Cui, P., & Zhu, W. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11271 · Cited by 64

Shifu2: A Network Representation Learning Based Model for Advisor-advisee Relationship Mining

Liu, J., Xia, F., Wang, L., Xu, B., Kong, X., Tong, H., & King, I. (2019) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2019.2946825 · Cited by 64

Heterogeneous graph-based joint representation learning for users and POIs in location-based social network

Qiao, Y., Luo, X., Li, C., Tian, H., & Ma, J. (2019) · Information Processing & Management · doi:10.1016/j.ipm.2019.102151 · Cited by 64

Dynamic Heterogeneous Graph Embedding Using Hierarchical Attentions

Yang, L., Xiao, Z., Jiang, W., Wei, Y., Hu, Y., & Wang, H. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-45442-5_53 · Cited by 65

Node similarity-based graph convolution for link prediction in biological networks

Coşkun, M., & Koyutürk, M. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab464 · Cited by 65

Fauce

Liu, J., Dong, W., Zhou, Q., & Li, D. (2021) · Proceedings of the VLDB Endowment · doi:10.14778/3476249.3476254 · Cited by 64

Pre-training Methods in Information Retrieval

Fan, Y., Xie, X., Cai, Y., Chen, J., Ma, X., Li, X., Zhang, R., & Guo, J. (2022) · Foundations and Trends® in Information Retrieval · doi:10.1561/1500000100 · Cited by 69

Self-Paced Co-Training of Graph Neural Networks for Semi-Supervised Node Classification

Gong, M., Zhou, H., Qin, A. K., Liu, W., & Zhao, Z. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3157688 · Cited by 64

Domain Adversarial Spatial-Temporal Network

Tang, Y., Qu, A., Chow, A. H. F., Lam, W. H. K., Wong, S. C., & Ma, W. (2022) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3511808.3557294 · Cited by 70

Predicting Drug-Target Interactions Over Heterogeneous Information Network

Su, X., Hu, P., Yi, H.-C., You, Z., & Hu, L. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3219213 · Cited by 66

Theoretical Foundations and Limits of Word Embeddings: What Types of Meaning can They Capture?

Arseniev‐Koehler, A. (2022) · Sociological Methods & Research · doi:10.1177/00491241221140142 · Cited by 75

Complex systems and network science: a survey

Yang, K., Li, J., Liu, M., Lei, T., Xu, X., Wu, H., Cao, J., & Qi, G. (2023) · Journal of Systems Engineering and Electronics · doi:10.23919/jsee.2023.000080 · Cited by 64

SkeletonMAE: Graph-based Masked Autoencoder for Skeleton Sequence Pre-training

Yan, H., Liu, Y., Wei, Y., Li, Z., Li, G., & Lin, L. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00516 · Cited by 76

Embedding Learning with Events in Heterogeneous Information Networks

Gui, H., Liu, J., Tao, F., Jiang, M., Norick, B., Kaplan, L., & Han, J. (2017) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2017.2733530 · Cited by 65

Computational Screening of Phase-Separating Proteins

Shen, B., Chen, Z., Yu, C., Chen, T., Shi, M., & Li, T. (2021) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2020.11.003 · Cited by 67

Graph Partition Neural Networks for Semi-Supervised ClassificationPreprint

Liao, R., Brockschmidt, M., Tarlow, D., Gaunt, A. L., Urtasun, R., & Zemel, R. S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.06272 · Cited by 65

DeepCas: an End-to-end Predictor of Information CascadesPreprint

Li, C., Ma, J., Guo, X., & Mei, Q. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.05373 · Cited by 65

Familial Clustering for Weakly-Labeled Android Malware Using Hybrid Representation Learning

Zhang, Y., Sui, Y., Pan, S., Zheng, Z., Ning, B., Tsang, I. W.-H., & Zhou, W. (2019) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2019.2947861 · Cited by 63

Detecting Lateral Movement in Enterprise Computer Networks with Unsupervised Graph {AI}

Bowman, B., Laprade, C. A., Ji, Y., & Huang, H. H. (2020) · Recent Advances in Intrusion Detection · Cited by 63

Deep fusion of multimodal features for social media retweet time prediction

Yin, H., Yang, S., Song, X., Liu, W., & Li, J. (2020) · World Wide Web · doi:10.1007/s11280-020-00850-7 · Cited by 63

Predicting dengue outbreaks at neighbourhood level using human mobility in urban areas

Bomfim, R., Pei, S., Shaman, J. L., Yamana, T. K., Makse, H. A., Andrade, J. S., Neto, A. S. L., & Furtado, V. (2020) · Journal of The Royal Society Interface · doi:10.1098/rsif.2020.0691 · Cited by 66

Identification of Protein Subcellular Localization With Network and Functional Embeddings

Pan, X., Li, H., Zeng, T., Li, Z., Chen, L., Huang, T., & Cai, Y. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2020.626500 · Cited by 63

Towards Multi-Modal Conversational Information Seeking

Deldjoo, Y., Trippas, J. R., & Zamani, H. (2021) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3404835.3462806 · Cited by 66

BRIGHT: A Bridging Algorithm for Network Alignment

Yan, Y., Zhang, S., & Tong, H. (2021) · International World Wide Web Conference (WWW) · doi:10.1145/3442381.3450053 · Cited by 64

A network embedding framework based on integrating multiplex network for drug combination prediction

Yu, L., Xia, M., & An, Q. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab364 · Cited by 63

Detecting Communities from Heterogeneous Graphs

Luo, L., Fang, Y., Cao, X., Zhang, X., & Zhang, W. (2021) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3459637.3482250 · Cited by 63

A Comprehensive Survey on Electronic Design Automation and Graph Neural Networks: Theory and Applications

Lopera, D. S., Servadei, L., Kiprit, G. N., Wille, R., & Ecker, W. (2022) · ACM Transactions on Design Automation of Electronic Systems · doi:10.1145/3543853 · Cited by 71

Knowledge Graphs and Explainable AI in Healthcare

Rajabi, E., & Kafaie, S. (2022) · Information · doi:10.3390/info13100459 · Cited by 73

Unsupervised graph-level representation learning with hierarchical contrasts

Ju, W., Gu, Y., Luo, X., Wang, Y., Yuan, H., Zhong, H., & Zhang, M. (2022) · Neural Networks · doi:10.1016/j.neunet.2022.11.019 · Cited by 66

Augmentation-Free Graph Contrastive Learning of Invariant-Discriminative Representations

Li, H., Cao, J., Zhu, J., Luo, Q., He, S., & Wang, X. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3248871 · Cited by 65

Disease Prediction Using Graph Machine Learning Based on Electronic Health Data: A Review of Approaches and Trends

Lu, H., & Uddin, S. (2023) · Healthcare · doi:10.3390/healthcare11071031 · Cited by 67

Graph Representation Learning and Its Applications: A Survey

Hoang, V. T., Jeon, H.-J., You, E.-S., Yoon, Y., Jung, S., & Lee, O. (2023) · Sensors · doi:10.3390/s23084168 · Cited by 68

A Survey on Hyperlink Prediction

Chen, C., & Liu, Y. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3286280 · Cited by 70

Software Defect Prediction Based on Deep Representation Learning of Source Code From Contextual Syntax and Semantic Graph

Abdu, A., Zhai, Z., Abdo, H. A., & Algabri, R. (2024) · IEEE Transactions on Reliability · doi:10.1109/tr.2024.3354965 · Cited by 63

Explaining the Explainers in Graph Neural Networks: a Comparative Study

Longa, A., Azzolin, S., Santin, G., Cencetti, G., Lió, P., Lepri, B., & Passerini, A. (2024) · ACM Computing Surveys · doi:10.1145/3696444 · Cited by 83

Predictive Network Representation Learning for Link Prediction

Wang, Z., Chen, C., & Li, W. (2017) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3077136.3080692 · Cited by 63

Content to Node

Liu, J., He, Z., Wei, L., & Huang, Y. (2018) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3219819.3219988 · Cited by 66

Deep Knowledge Tracing with Side Information

Wang, Z., Feng, X., Tang, J., Huang, G. Y., & Liu, Z. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-23207-8_56 · Cited by 64

Target Defense Against Link-Prediction-Based Attacks via Evolutionary Perturbations

Yu, S., Zhao, M., Fu, C., Zheng, J., Huang, H., Shu, X., Xuan, Q., & Chen, G. (2019) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2019.2933833 · Cited by 63

Unsupervised Representation Learning of Spatial Data via Multimodal Embedding

Jenkins, P., Farag, A., Wang, S., & Li, Z. (2019) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3357384.3358001 · Cited by 63

An end-to-end neighborhood-based interaction model for knowledge-enhanced recommendation

Qu, Y., Bai, T., Zhang, W., Nie, J., & Tang, J. (2019) · doi:10.1145/3326937.3341257 · Cited by 63

GL2vec: Graph Embedding Enriched by Line Graphs with Edge Features

Chen, H., & Koga, H. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-36718-3_1 · Cited by 63

TIMME

Xiao, Z., Song, W., Xu, H., Ren, Z., & Sun, Y. (2020) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3394486.3403275 · Cited by 63

Contrastive Learning of Structured World Models

Kipf, T., van der Pol, E., & Welling, M. (2020) · Data Archiving and Networked Services (DANS) · Cited by 63

Characterizing and Detecting Money Laundering Activities on the Bitcoin NetworkPreprint

Hu, Y., Seneviratne, S., Thilakarathna, K., Fukuda, K., & Seneviratne, A. P. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.12060 · Cited by 63

GraphCL: Contrastive Self-Supervised Learning of Graph RepresentationsPreprint

Hafidi, H., Ghogho, M., Ciblat, P., & Swami, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.08025 · Cited by 63

GNNAdvisor: An Adaptive and Efficient Runtime System for GNN Acceleration on GPUsPreprint

Wang, Y., Feng, B., Li, G., Li, S., Deng, L., Xie, Y., & Ding, Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.06608 · Cited by 63

Combating Crowdsourced Review Manipulators: A Neighborhood-Based Approach

Kaghazgaran, P., Caverlee, J., & Squicciarini, A. (2018) · Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining · doi:10.1145/3159652.3159726 · Cited by 59

PRIMAL

Zhou, Y. J., Ren, H. M., Zhang, Y., Keller, B., Khailany, B., & Zhang, Z. (2019) · ACM/IEEE Design Automation Conference (DAC) · doi:10.1145/3316781.3317884 · Cited by 68

Scalable Graph Embeddings via Sparse Transpose Proximities

Yin, Y., & Wei, Z. (2019) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3292500.3330860 · Cited by 62

Label-Aware Graph Convolutional Networks

Chen, H., Xu, Y., Huang, F., Deng, Z., Huang, W., Wang, S., He, P. J., & Li, Z. (2020) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3340531.3412139 · Cited by 62

Leveraging Meta-path Contexts for Classification in Heterogeneous Information Networks

Li, X., Ding, D., Kao, B., Sun, Y., & Mamoulis, N. (2021) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde51399.2021.00084 · Cited by 63

Link prediction by deep non-negative matrix factorization

Chen, G., Wang, H., Fang, Y., & Jiang, L. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115991 · Cited by 62

Community Detection in Graph: An Embedding Method

Zhu, J., Wang, C., Gao, C., Zhang, F., Wang, Z., & Li, X. (2021) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2021.3130321 · Cited by 62

CasSeqGCN: Combining network structure and temporal sequence to predict information cascades

Wang, Y., Wang, X., Ran, Y., Michalski, R., & Jia, T. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.117693 · Cited by 63

DyGCN: Efficient Dynamic Graph Embedding With Graph Convolutional Network

Cui, Z., Li, Z., Wu, S., Zhang, X., Liu, Q., Wang, L., & Ai, M. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3185527 · Cited by 62

Temporal Link Prediction: A Unified Framework, Taxonomy, and Review

Qin, M., & Yeung, D. (2023) · ACM Computing Surveys · doi:10.1145/3625820 · Cited by 69

Predicting Twitter User Socioeconomic Attributes with Network and Language Information

Αλέτρας, Ν., & Chamberlain, B. P. (2018) · ACM Conference on Hypertext and Social Media (HT) · doi:10.1145/3209542.3209577 · Cited by 64

Surrogate-Assisted Robust Optimization of Large-Scale Networks Based on Graph Embedding

Wang, S., Liu, J., & Jin, Y. (2019) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2019.2950935 · Cited by 62

Learning on knowledge graph dynamics provides an early warning of impactful research

Weis, J. W., & Jacobson, J. M. (2021) · Nature Biotechnology · doi:10.1038/s41587-021-00907-6 · Cited by 62

Employee profiling via aspect-based sentiment and network for insider threats detection

Soh, C., Yu, S., Narayanan, A., Duraisamy, S., & Chen, L. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.05.043 · Cited by 61

A Community Detection Method for Social Network Based on Community Embedding

Li, M., Lu, S., Zhang, L., Zhang, Y., & Zhang, B. (2021) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2021.3050397 · Cited by 61

An overview of biomedical entity linking throughout the years

French, E., & McInnes, B. T. (2022) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2022.104252 · Cited by 68

Node Connection Strength Matrix-Based Graph Convolution Network for Traffic Flow Prediction

Chen, J., Wang, W. W., Yu, K., Hu, X., Cai, M., & Guizani, M. (2023) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2023.3265300 · Cited by 63

Artificial intelligence-driven biomedical genomics

Guo, K., Wu, M., Soo, Z., Yang, Y. Y., Zhang, Y., Zhang, Q., Lin, H., Grosser, M., Venter, D. J., Zhang, G., et al. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110937 · Cited by 64

A Comprehensive Survey on Graph Summarization With Graph Neural Networks

Shabani, N., Wu, J. X., Beheshti, A., Sheng, Q. Z., Foo, J., Haghighi, V., Hanif, A., & Shahabikargar, M. (2024) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2024.3350545 · Cited by 66

Dynamic Graph Representation Learning With Neural Networks: A Survey

Yang, L., Chatelain, C., & Adam, S. (2024) · IEEE Access · doi:10.1109/access.2024.3378111 · Cited by 71

User community detection via embedding of social network structure and temporal content

Fani, H., Jiang, E., Ebrahim Bagheri, A., Al-Obeidat, F. N., Du, W., & Kargar, M. (2019) · Information Processing & Management · doi:10.1016/j.ipm.2019.102056 · Cited by 61

NodeSketch

Yang, D., Rosso, P., Li, B., & Cudré-Mauroux, P. (2019) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3292500.3330951 · Cited by 61

Cross-Language Clone Detection by Learning Over Abstract Syntax Trees

Pérez, D., & Chiba, S. (2019) · International Conference on Mining Software Repositories (MSR) · doi:10.1109/msr.2019.00078 · Cited by 64

Out-of-sample Node Representation Learning for Heterogeneous Graph in Real-time Android Malware Detection

Ye, Y., Hou, S., Chen, L., Lei, J., Wan, W., Wang, J., Xiong, Q., & Shao, F. (2019) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/576 · Cited by 61

EmbeddingVis: A Visual Analytics Approach to Comparative Network Embedding Inspection

Li, Q., Njotoprawiro, K. S., Haleem, H., Chen, Q., Yi, C., & Ma, X. (2018) · IEEE Conference on Visual Analytics Science and Technology (VAST) · doi:10.1109/vast.2018.8802454 · Cited by 61

Your Style Your Identity: Leveraging Writing and Photography Styles for Drug Trafficker Identification in Darknet Markets over Attributed Heterogeneous Information Network

Zhang, Y., Fan, Y., Song, W. G., Hou, S., Ye, Y., Li, X., Zhao, L., Shi, C., Wang, J., & Xiong, Q. (2019) · International World Wide Web Conference (WWW) · doi:10.1145/3308558.3313537 · Cited by 61

Representation Learning over Dynamic GraphsPreprint

Trivedi, R., Farajtabar, M., Biswal, P., & Hongyuan Zha, B. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.04051 · Cited by 61

Discrete Network Embedding

Shen, X., Pan, S., Liu, W., Ong, Y.-S., & Sun, Q.-S. (2018) · Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/493 · Cited by 57

gat2vec: representation learning for attributed graphs

Sheikh, N., Kefato, Z. T., & Montresor, A. (2018) · Computing · doi:10.1007/s00607-018-0622-9 · Cited by 60

Using K-core Decomposition on Class Dependency Networks to Improve Bug Prediction Model's Practical Performance

Qu, Y., Zheng, Q., Chi, J., Jin, Y., He, A., Cui, D., Zhang, H., & Liu, T. (2019) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2019.2892959 · Cited by 60

Network Embedding and Change Modeling in Dynamic Heterogeneous Networks

Bian, R., Koh, Y. S., Dobbie, G., & Divoli, A. (2019) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3331184.3331273 · Cited by 60

Simple and Effective Graph Autoencoders with One-Hop Linear Models

Salha-Galvan, G., Hennequin, R., & Vazirgiannis, M. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-67658-2_19 · Cited by 60

Continuous-Time Dynamic Graph Learning via Neural Interaction Processes

Chang, X., Liu, X., Wen, J., Li, S., Fang, Y., Song, L., & Qi, Y. (2020) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3340531.3411946 · Cited by 63

Self-Supervised Learning of Contextual Embeddings for Link Prediction in Heterogeneous Networks

Wang, P., Agarwal, K., Ham, C., Choudhury, S., & Reddy, C. K. A. (2021) · International World Wide Web Conference (WWW) · doi:10.1145/3442381.3450060 · Cited by 60

Principled approach to the selection of the embedding dimension of networks

Gu, W., Tandon, A., Ahn, Y., & Radicchi, F. (2021) · Nature Communications · doi:10.1038/s41467-021-23795-5 · Cited by 60

Signed Bipartite Graph Neural Networks

Huang, J., Shen, H., Cao, Q., Tao, S., & Cheng, X. (2021) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3459637.3482392 · Cited by 60

Graph Communal Contrastive Learning

Li, B., Jing, B., & Tong, H. (2022) · International World Wide Web Conference (WWW) · doi:10.1145/3485447.3512208 · Cited by 60

HGATE: Heterogeneous Graph Attention Auto-Encoders

Wang, W., Suo, X., Wei, X., Wang, B., Wang, H. H., Dai, H., & Zhang, X. (2021) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3138788 · Cited by 61

Systems pharmacology approaches in herbal medicine research: a brief review

Lee, M., Shin, H., Park, M., Kim, A., Cha, S., & Lee, H. (2022) · BMB Reports · doi:10.5483/bmbrep.2022.55.9.102 · Cited by 62

GCNET: Graph-based prediction of stock price movement using graph convolutional network

Jafari, A., & Haratizadeh, S. (2022) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.105452 · Cited by 60

Struct2GO: protein function prediction based on graph pooling algorithm and AlphaFold2 structure information

Jiao, P., Wang, B., Wang, X., Liu, B., Wang, Y., & Li, J. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad637 · Cited by 65

Knowledge Graphs for drug repurposing: a review of databases and methods

Perdomo-Quinteiro, P., & Belmonte-Hernández, A. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae461 · Cited by 66

Class-Imbalanced Learning on Graphs: A Survey

Ma, Y., Tian, Y., Moniz, N., & Chawla, N. V. (2025) · ACM Computing Surveys · doi:10.1145/3718734 · Cited by 74

Cross-Network Skip-Gram Embedding for Joint Network Alignment and Link Prediction

Du, X., Yan, J., Zhang, R., & Hongyuan Zha, B. (2020) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2020.2997861 · Cited by 60

Transfer Learning of Graph Neural Networks with Ego-graph Information MaximizationPreprint

Zhu, Q., Yang, C., Xu, Y., Wang, H., Zhang, C., & Han, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.05204 · Cited by 60

Graph InfoClust: Leveraging cluster-level node information for unsupervised graph representation learningPreprint

Mavromatis, C., & Karypis, G. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.06946 · Cited by 72

Learning Conjoint Attentions for Graph Neural NetsPreprint

He, T., Ong, Y.-S., & Bai, L. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.03147 · Cited by 60

Integrating and formatting biomedical data as pre-calculated knowledge graph embeddings in the Bioteque

Fernández-Torras, A., Duran-Frigola, M., Bertoni, M., Locatelli, M., & Aloy, P. (2022) · Nature Communications · doi:10.1038/s41467-022-33026-0 · Cited by 59

A General View for Network Embedding as Matrix Factorization

Liu, X., Murata, T., Kim, K., Kotarasu, C., & Zhuang, C. (2019) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3289600.3291029 · Cited by 59

CaEGCN: Cross-Attention Fusion Based Enhanced Graph Convolutional Network for Clustering

Huo, G., Zhang, Y., Gao, J., Wang, B., Hu, Y., & Yin, B. (2021) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3125020 · Cited by 61

A Hierarchical Approach for Advanced Persistent Threat Detection with Attention-Based Graph Neural Networks

Li, Z., Cheng, X., Sun, L., Zhang, J., & Chen, B. (2021) · Security and Communication Networks · doi:10.1155/2021/9961342 · Cited by 61

Unsupervised Path Representation Learning with Curriculum Negative Sampling

Yang, B., Guo, C., Hu, J., Tang, J., & Yang, B. (2021) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2021/452 · Cited by 68

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