The DOI matches its Crossref record; 34 of the 38 references it cites were found in a registry or an index.
node2vec: Scalable Feature Learning for Networks
Abstract
Prediction tasks over nodes and edges in networks require careful effort in engineering features used by learning algorithms. Recent research in the broader field of representation learning has led to significant progress in automating prediction by learning the features themselves. However, present feature learning approaches are not expressive enough to capture the diversity of connectivity patterns observed in networks. Here we propose node2vec, an algorithmic framework for learning continuous feature representations for nodes in networks. In node2vec, we learn a mapping of nodes to a low-dimensional space of features that maximizes the likelihood of preserving network neighborhoods of nodes. We define a flexible notion of a node's network neighborhood and design a biased random walk procedure, which efficiently explores diverse neighborhoods. Our algorithm generalizes prior work which is based on rigid notions of network neighborhoods, and we argue that the added flexibility in exploring neighborhoods is the key to learning richer representations.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“graph embedding methods like node2vec [17] and DeepWalk [26] cannot be applied here.”
From Graph Convolutional Neural Networks for Web-Scale Recommender Systems (2018) · cites this paper as [17]
Citations
Cites
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
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
Research works printed but not linked: 10
- Belkin M., 2001, NIPS
- Harris Z. S., 1954, Word. Distributional Structure, 10, 146
- Li Y., 2016, ICLR
- 10.1002/asi.v58:7 · DOI
- Mahoney M., 2011, Large text compression benchmark. www.mattmahoney.net/dc/textdata
- Mikolov T., 2013, ICLR
- Mikolov T., 2013, NIPS
- Recht B., 2011, NIPS
- Tian F., 2014, AAAI
- Zafarani R., 2009, Social computing data repository at ASU
Cited by
Sorted by citations
Items 401–500 of 1,030
Improving cancer driver gene identification using multi-task learning on graph convolutional network
Peng, W., Tang, Q., Dai, W., & Chen, T. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab432 · Cited by 132
Towards multimodal foundation models in molecular cell biology
Cui, H., Tejada-Lapuerta, A., Brbić, M., Sáez-Rodríguez, J., Cristea, S., Goodarzi, H., Lotfollahi, M., Theis, F. J., & Wang, B. (2025) · Nature · doi:10.1038/s41586-025-08710-y · Cited by 151
Introduction to Graph Neural Networks
Liu, Z., & Zhou, J. (2020) · Synthesis lectures on artificial intelligence and machine learning · doi:10.1007/978-3-031-01587-8 · Cited by 139
Are Graph Convolutional Networks With Random Weights Feasible?
Huang, C., Li, M., Cao, F., Fujita, H., Li, Z., & Wu, X. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3183143 · Cited by 133
Adversarially Regularized Graph Autoencoder for Graph EmbeddingPreprint
Pan, S., Hu, R., Long, G., Jiang, J. B., Yao, L., & Zhang, C. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.04407 · Cited by 129
Applications of near-term photonic quantum computers: software and algorithms
Bromley, T. R., Arrazola, J. M., Jahangiri, S., Izaac, J., Quesada, N., Gran, A. D., Schuld, M., Swinarton, J., Zabaneh, Z., & Killoran, N. (2020) · Quantum Science and Technology · doi:10.1088/2058-9565/ab8504 · Cited by 128
Kong, X., Mao, M., Wang, W. W., Liu, J., & Xu, B. (2018) · IEEE Transactions on Emerging Topics in Computing · doi:10.1109/tetc.2018.2830698 · Cited by 128
Progresses and challenges in link prediction
Zhou, T. (2021) · iScience · doi:10.1016/j.isci.2021.103217 · Cited by 131
Adaptive Graph Auto-Encoder for General Data Clustering
Li, X., Zhang, H., & Zhang, R. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3125687 · Cited by 128
Generative Adversarial Networks for Spatio-temporal Data: A Survey
Gao, N., Xue, H., Shao, W., Zhao, S., Qin, K. K., Prabowo, A., Rahaman, M. S., & Salim, F. D. (2022) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3474838 · Cited by 127
Gao, Y., Li, X., Peng, H. M., Fang, B., & Yu, P. S. (2020) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2020.2987019 · Cited by 127
Survey on graph embeddings and their applications to machine learning problems on graphs
Makarov, I., Kiselev, D., Nikitinsky, N., & Šubelj, L. (2021) · PeerJ Computer Science · doi:10.7717/peerj-cs.357 · Cited by 135
Extending the small-molecule similarity principle to all levels of biology with the Chemical Checker
Duran-Frigola, M., Pauls, E., Guitart-Pla, O., Bertoni, M., Alcalde, V., Amat, D., Juan-Blanco, T., & Aloy, P. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0502-7 · Cited by 126
Easing Embedding Learning by Comprehensive Transcription of Heterogeneous Information Networks
Shi, Y., Zhu, Q., Guo, F., Zhang, C., & Han, J. (2018) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3219819.3220006 · Cited by 126
Deep Modeling of Social Relations for Recommendation
Fan, W., Li, Q., & Cheng, M. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.12132 · Cited by 126
Heterogeneous Multi-Layered Network Model for Omics Data Integration and Analysis
Lee, B., Zhang, S., Poleksić, A., & Xie, L. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2019.01381 · Cited by 126
Decoupling Representation Learning and Classification for GNN-based Anomaly Detection
Wang, Y., Zhang, J., Guo, S., Yin, H., Li, C., & Chen, H. (2021) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3404835.3462944 · Cited by 131
Michael Backes, A., Humbert, M., Pang, J., & Zhang, Y. (2017) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3133956.3133972 · Cited by 125
Higher-order Network Representation Learning
Rossi, R. A., Ahmed, N. K., & Koh, E. (2018) · International World Wide Web Conference (WWW) · doi:10.1145/3184558.3186900 · Cited by 125
Hyperbolic Graph Attention Network
Zhang, Y., Wang, X., Shi, C., Jiang, X., & Ye, Y. F. (2021) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2021.3081431 · Cited by 130
Qu, L., Zhu, H., Zheng, R., Shi, Y., & Yin, H. (2021) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3447548.3467334 · Cited by 129
Cheng, L., Li, J., Silva, Y. N., Hall, D. L., & Liu, H. (2019) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3289600.3291037 · Cited by 124
Graph convolutional networks for graphs containing missing features
Taguchi, H., Liu, X., & Murata, T. (2020) · Future Generation Computer Systems · doi:10.1016/j.future.2020.11.016 · Cited by 130
Quantifying Privacy Leakage in Graph Embedding
Duddu, V., Boutet, A., & Shejwalkar, V. (2020) · EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MobiQuitous) · doi:10.1145/3448891.3448939 · Cited by 136
A modified DeepWalk method for link prediction in attributed social network
Berahmand, K., Nasiri, E., Rostami, M., & Forouzandeh, S. (2021) · Computing · doi:10.1007/s00607-021-00982-2 · Cited by 124
Zhou, W., Yang, K., Zeng, J., Lai, X., Wang, X., Ji, C., Li, Y., Zhang, P., & Li, S. (2021) · Pharmacological Research · doi:10.1016/j.phrs.2021.105752 · Cited by 124
CATCHM: A novel network-based credit card fraud detection method using node representation learning
Belle, R. V., Baesens, B., & Weerdt, J. D. (2022) · Decision Support Systems · doi:10.1016/j.dss.2022.113866 · Cited by 124
He, Y., Song, Y., Li, J., Ji, C., Peng, J., & Peng, H. M. (2019) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3357384.3358061 · Cited by 123
A comprehensive review of recommender systems: Transitioning from theory to practice
Raza, S., Rahman, M., Kamawal, S., Toroghi, A., Raval, A., Navah, F., & Kazemeini, A. (2025) · Computer Science Review · doi:10.1016/j.cosrev.2025.100849 · Cited by 159
The Future of False Information Detection on Social Media: New Perspectives and Trends
Guo, B., Ding, Y., Yao, L., Liang, Y., & Yu, Z. (2021) · ACM Computing Surveys · doi:10.1145/3393880 · Cited by 122
Region Representation Learning via Mobility Flow
Wang, H., & Li, Z. (2017) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3132847.3133006 · Cited by 122
MetaGraph2Vec: Complex Semantic Path Augmented Heterogeneous Network Embedding
Zhang, D., Yin, J., Xingquan Zhu, A., & Zhang, C. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-93037-4_16 · Cited by 122
Detecting Phishing Scams on Ethereum Based on Transaction Records
Yuan, Q., Huang, B., Zhang, J., Wu, J., Zhang, H., & Zhang, X. (2020) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas45731.2020.9180815 · Cited by 122
Heterogeneous Hypergraph Embedding for Graph Classification
Sun, X., Yin, H., Liu, B., Chen, H., Cao, J., Shao, Y., & Nguyen, Q. V. H. (2021) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3437963.3441835 · Cited by 127
Link Weight Prediction Using Supervised Learning Methods and Its Application to Yelp Layered Network
Fu, C., Zhao, M., Fan, L., Chen, X., Chen, J., Wu, Z., Xia, Y., & Xuan, Q. (2018) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2018.2801854 · Cited by 122
Hyperbolic Heterogeneous Information Network Embedding
Wang, X., Zhang, Y., & Shi, C. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33015337 · Cited by 121
A community detection algorithm based on graph compression for large-scale social networks
Zhao, X., Liang, J., & Wang, J. (2020) · Information Sciences · doi:10.1016/j.ins.2020.10.057 · Cited by 121
GraphSynergy: a network-inspired deep learning model for anticancer drug combination prediction
Yang, J., Xu, Z., Wu, W. K. K., Chu, Q., & Zhang, Q. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocab162 · Cited by 121
Comprehensive evaluation of deep and graph learning on drug–drug interactions prediction
Lin, X., Dai, L., Zhou, Y., Yu, Z., Zhang, W., Shi, J., Cao, D., Li, Z., Chen, H., Song, B., et al. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad235 · Cited by 124
Rosenthal, G., Váša, F., Griffa, A., Hagmann, P., Amico, E., Goñi, J., Avidan, G., & Sporns, O. (2018) · Nature Communications · doi:10.1038/s41467-018-04614-w · Cited by 127
Cross-Network Embedding for Multi-Network Alignment
Chu, X., Fan, X., Yao, D., Zhu, Z., Huang, J., & Bi, J. (2019) · International World Wide Web Conference (WWW) · doi:10.1145/3308558.3313499 · Cited by 120
Meng, W., Yang, D., & Xiao, Y. (2020) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3397271.3401098 · Cited by 125
Estimating urban functional distributions with semantics preserved POI embedding
Huang, W., Cui, L., Chen, M., Zhang, D., & Yao, Y. (2022) · International Journal of Geographical Information Systems · doi:10.1080/13658816.2022.2040510 · Cited by 123
Internet Financial Fraud Detection Based on Graph Learning
Li, R., Liu, Z., Ma, Y., Yang, D., & Sun, S. (2023) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2022.3189368 · Cited by 106
Multiplex Heterogeneous Graph Convolutional Network
Yu, P., Fu, C., Yu, Y., Huang, C., Zhao, Z., & Dong, J. (2022) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3534678.3539482 · Cited by 120
dynnode2vec: Scalable Dynamic Network Embedding
Mahdavi, S., Khoshraftar, S., & An, A. (2018) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2018.8621910 · Cited by 130
N-Gram Graph: Simple Unsupervised Representation for Graphs, with Applications to MoleculesPreprint
Liu, S., Demirel, M. F., & Liang, Y. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.09206 · Cited by 125
A Unified Framework for Community Detection and Network Representation Learning
Tu, C., Zeng, X., Wang, H., Zhang, Z., Liu, Z., Sun, M., Zhang, B., & Lin, L. (2018) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2018.2852958 · Cited by 120
Adversarial Attacks on Graph Neural Networks
Zügner, D., Borchert, O., Akbarnejad, A., & Günnemann, S. (2020) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3394520 · Cited by 122
An Efficient Neighborhood-based Interaction Model for Recommendation on Heterogeneous Graph
Jin, J., Qin, J., Fang, Y., Du, K., Zhang, W., Yu, Y. H., Zhang, Z., & Smola, A. J. (2020) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3394486.3403050 · Cited by 118
WATSON: Abstracting Behaviors from Audit Logs via Aggregation of Contextual Semantics
Zeng, J., Chua, Z. L., Chen, Y., Ji, K., Liang, Z., & Mao, J. (2021) · Network and Distributed System Security Symposium · doi:10.14722/ndss.2021.24549 · Cited by 126
Graph Transfer Learning via Adversarial Domain Adaptation with Graph Convolution
Dai, Q., Wu, X.-M., Xiao, J., Shen, X. X., & Wang, D. (2022) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2022.3144250 · Cited by 121
Bootstrapped Representation Learning on GraphsPreprint
Thakoor, S., Tallec, C., Azar, M. G., Munos, R., Veličković, P., & Valko, M. (2021) · arXiv (Cornell University) · Cited by 118
Collaborative User Network Embedding for Social Recommender Systems
Zhang, C., Yu, L., Wang, Y., Shah, C. S., & Zhang, X. (2017) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9781611974973.43 · Cited by 117
Co-Embedding of Nodes and Edges With Graph Neural Networks
Jiang, X., Zhu, R., Ji, P., & Li, S. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3029762 · Cited by 122
Current and future directions in network biology
Žitnik, M., Li, M. M., Wells, A., Glass, K., Gysi, D. M., Krishnan, A., Murali, T. M., Predrag Radivojac, A., Roy, S., Baudot, A., et al. (2024) · Bioinformatics Advances · doi:10.1093/bioadv/vbae099 · Cited by 128
High Performance Graph Convolutional Networks with Applications in Testability Analysis
Ma, Y., Ren, H. M., Khailany, B., Sikka, H., Luo, L., Natarajan, K., & Yu, B. (2019) · ACM/IEEE Design Automation Conference (DAC) · doi:10.1145/3316781.3317838 · Cited by 121
Zhang, Y., Fan, Y., Ye, Y., Zhao, L., & Shi, C. (2019) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3357384.3357876 · Cited by 116
Deep Learning for Android Malware Defenses: A Systematic Literature Review
Liu, Y., Tantithamthavorn, C. K., Li, L., & Liu, Y. (2022) · ACM Computing Surveys · doi:10.1145/3544968 · Cited by 123
Berahmand, K., Nasiri, E., Mohammadiani, R. P., & Li, Y. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104933 · Cited by 119
Zhang, Y., Zhu, H., Song, Z., Koniusz, P., & King, I. (2022) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3534678.3539425 · Cited by 123
S2GAE: Self-Supervised Graph Autoencoders are Generalizable Learners with Graph Masking
Tan, Q., Liu, N., Huang, X. X., Choi, S.-H., Li, L., Chen, R., & Hu, X. (2023) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3539597.3570404 · Cited by 119
A comprehensive review of community detection in graphs
Li, J., Lai, S., Shuai, Z., Tan, Y., Jia, Y., Yu, M., Song, Z., Peng, X., Xu, Z., Ni, Y., et al. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128169 · Cited by 135
Hyperbolic Entailment Cones for Learning Hierarchical Embeddings
Ganea, O.-E., Bécigneul, G., & Hofmann, T. (2018) · arXiv · doi:10.48550/arxiv.1804.01882 · Cited by 115
“A large line of research focuses on embedding discrete data such as graphs (Grover & Leskovec, 2016; Goyal & Ferrara, 2017) or linguistic instances (Mikolov et al., 2013; Pennington et al., 2014; Kiros et al., 2015) into continuous spaces that exhibit certain desirable geometric properties.”
BackgroundHussein, R., Yang, D., & Cudré-Mauroux, P. (2018) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3269206.3271777 · Cited by 115
Effective Travel Time Estimation: When Historical Trajectories over Road Networks Matter
Yuan, H., Li, G., Bao, Z., & Feng, L. (2020) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/3318464.3389771 · Cited by 117
Enhancing Drug-Drug Interaction Prediction Using Deep Attention Neural Networks
Liu, S., Zhang, Y., Cui, Y., Qiu, Y., Deng, Y., Zhang, W., & Zhang, W. (2022) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2022.3172421 · Cited by 115
Predicting Parkinson's Disease Genes Based on Node2vec and Autoencoder
Peng, J., Guan, J., & Shang, X. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00226 · Cited by 114
Deep Reinforcement Learning-Based Approach to Tackle Topic-Aware Influence Maximization
Tian, S., Mo, S., Wang, L., & Peng, Z. (2020) · Data Science and Engineering · doi:10.1007/s41019-020-00117-1 · Cited by 114
Wu, H., Luo, X., Zhou, M., Rawa, M. J. H., Sedraoui, K., & Albeshri, A. A. (2021) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2021.1004308 · Cited by 114
Zhou, J., Chen, L., & Guo, Z. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz757 · Cited by 114
Sankar, A., Zhang, X., Krishnan, A., & Han, J. (2020) · Web Search and Data Mining · doi:10.1145/3336191.3371811 · Cited by 126
InFoRM: Individual Fairness on Graph Mining
Kang, J. L., He, J., Maciejewski, R., & Tong, H. (2020) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3394486.3403080 · Cited by 113
Behavior-Aware Account De-Anonymization on Ethereum Interaction Graph
Zhou, J., Hu, C., Chi, J., Wu, J., Shen, M., & Xuan, Q. (2022) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2022.3208471 · Cited by 113
Hybrid Graph Convolutional Networks for Semi-Supervised Classification
(2019) · International Workshop on Computer Science and Engineering (WCSE) · doi:10.18178/wcse.2019.06.016 · Cited by 113
Zhu, J., Dai, Q., Su, L., Ma, R., Liu, J., Cai, G., Xiao, X., & Zhang, R. (2022) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3477495.3531723 · Cited by 127
GAD-NR: Graph Anomaly Detection via Neighborhood Reconstruction
Roy, A., Shu, J., Li, J., Yang, C., Elshocht, O., Smeets, J., & Li, P. (2024) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3616855.3635767 · Cited by 122
Self-Supervised Temporal Graph Learning With Temporal and Structural Intensity Alignment
Liu, M., Liang, K., Zhao, Y., Tu, W., Zhou, S., Gan, X., Liu, X., & He, K. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3386168 · Cited by 121
Name Disambiguation in Anonymized Graphs using Network Embedding
Zhang, B., & Hasan, M. A. (2017) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3132847.3132873 · Cited by 112
Network Embedding for Community Detection in Attributed Networks
Sun, H., He, F., Huang, J., Sun, Y., Yang, L., Wang, C., He, L., Sun, Z., & Jia, X. (2020) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3385415 · Cited by 112
Blockchain is Watching You: Profiling and Deanonymizing Ethereum Users
Béres, F., Seres, I. A., Benczúr, A. A., & Quintyne-Collins, M. (2021) · IEEE International Conference on Decentralized Applications and Infrastructures (DAPPS) · doi:10.1109/dapps52256.2021.00013 · Cited by 112
Heterogeneous Network Representation Learning
Dong, Y., Hu, Z., Wang, K., Sun, Y., & Tang, J. (2020) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2020/677 · Cited by 114
ParaGraph: Layout Parasitics and Device Parameter Prediction using Graph Neural Networks
Ren, H. M., Kokai, G. F., Turner, W. J., & Ku, T.-S. (2020) · ACM/IEEE Design Automation Conference (DAC) · doi:10.1109/dac18072.2020.9218515 · Cited by 112
Deep attributed network representation learning of complex coupling and interaction
Li, Z., Wang, X., Li, J., & Zhang, Q. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.106618 · Cited by 112
From single- to multi-modal remote sensing imagery interpretation: a survey and taxonomy
Sun, X., Tian, Y., Lu, W., Wang, P., Niu, R., Yu, H., & Fu, K. (2023) · Science China Information Sciences · doi:10.1007/s11432-022-3588-0 · Cited by 119
Self-supervised Trajectory Representation Learning with Temporal Regularities and Travel Semantics
Jiang, J., Pan, D., Ren, H., Jiang, X., Li, C., & Wang, J. (2023) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde55515.2023.00070 · Cited by 120
Multi-Source and Multi-modal Deep Network Embedding for Cross-Network Node Classification
Yang, H., He, H. X., Zhang, W., Wang, Y., & Lin, J. (2024) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3653304 · Cited by 112
User Profiling through Deep Multimodal Fusion
Farnadi, G., Tang, J., Cock, M. D., & Moens, M. (2018) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3159652.3159691 · Cited by 112
Heterogeneous Hypergraph Variational Autoencoder for Link Prediction
Fan, H., Zhang, F., Wei, Y., Li, Z., Zou, C., Gao, Y., & Dai, Q. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3059313 · Cited by 116
Abdelrahman, M., Chong, A., & Miller, C. C. (2021) · Building and Environment · doi:10.1016/j.buildenv.2021.108532 · Cited by 111
Drug-drug interactions prediction based on deep learning and knowledge graph: A review
Luo, H., Yin, W., Wang, J., Zhang, G., Liang, W., Luo, J., & Yan, C. (2024) · iScience · doi:10.1016/j.isci.2024.109148 · Cited by 119
Graph Foundation Models: Concepts, Opportunities and Challenges
Liu, J., Yang, C. H., Lu, Z., Chen, J., Li, Y., Zhang, M., Bai, T., Fang, Y., Sun, L., Yu, P. S., et al. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3548729 · Cited by 217
A systemic analysis of link prediction in social network
Haghani, S., & Keyvanpour, M. R. (2017) · Artificial Intelligence Review · doi:10.1007/s10462-017-9590-2 · Cited by 111
FinBrain: when finance meets AI 2.0
Zheng, X., Zhu, M., Li, Q., Chen, C., & Tan, Y. (2019) · Frontiers of Information Technology & Electronic Engineering · doi:10.1631/fitee.1700822 · Cited by 121
Knowledge Graph-based Event Embedding Framework for Financial Quantitative Investments
Cheng, D., Yang, F., Wang, X., Zhang, Y., & Zhang, L. (2020) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3397271.3401427 · Cited by 110
Internet Financial Fraud Detection Based on a Distributed Big Data Approach With Node2vec
Zhou, H., Sun, G., Fu, S., Wang, L., Hu, J., & Gao, Y. (2021) · IEEE Access · doi:10.1109/access.2021.3062467 · Cited by 126
Park, N., Liu, F., Mehta, P., Cristofor, D., Faloutsos, C., & Dong, Y. (2022) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3488560.3498451 · Cited by 110
PIANO: Influence Maximization Meets Deep Reinforcement Learning
Li, H., Xu, M., Bhowmick, S. S., Rayhan, J. S., Sun, C., & Cui, J. (2022) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2022.3164667 · Cited by 110
A Survey of Graph-Based Deep Learning for Anomaly Detection in Distributed Systems
Pazho, A. D., Noghre, G. A., Purkayastha, A. A., Vempati, J., Martin, O., & Tabkhi, H. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2023.3282898 · Cited by 117
Contrastive Learning of Structured World ModelsPreprint
Kipf, T., van der Pol, E., & Welling, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.12247 · Cited by 180
Metadata
- Authors
- Grover, A., & Leskovec, J.
- Affiliations
- Stanford University (US)
- Venue
- Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
- Publisher
- ACM
- Published
- 2016-08-13
- Pages
- 855-864
- OpenAlex
- W2962756421
- Cited by
- 10,926
- References
- 38
- Citations by year
- 2026: 594 · 2025: 1153 · 2024: 1271 · 2023: 1349 · 2022: 1336 · 2021: 1660 · 2020: 1472 · 2019: 1183 · 2018: 663 · 2017: 217 · 2016: 23
- FWCI
- 367.64
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) (publishedVersion)
- Funders
- National Institute of Biomedical Imaging and Bioengineering (U54EB020405)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Topological and Geometric Data Analysis
- Keywords
- node2vec, node representation learning, graph representation learning, biased random walk, node embeddings, graph neural networks, edge prediction
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
