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.
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Graph embedding methods like node2vec and DeepWalk cannot be applied to the Pinterest setting.
“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]
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Fortunato, S. (2010) · Physics Reports · doi:10.1016/j.physrep.2009.11.002 · Cited by 11,293 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Gated Graph Sequence Neural NetworksPreprint
Li, Y., Tarlow, D., Brockschmidt, M., & Zemel, R. (2015) · International Conference on Learning Representations · doi:10.48550/arxiv.1511.05493 · Cited by 3,735 · see citing works
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
Yan, S., Xu, D., Zhang, B., Zhang, H.-J., Yang, Q., & Lin, S. C.-F. (2006) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.250598 · Cited by 3,035 · see citing works
Friends and neighbors on the Web
Adamic, L. A., & Adar, E. (2003) · Social Networks · doi:10.1016/s0378-8733(03)00009-1 · Cited by 2,989 · see citing works
Feature-rich part-of-speech tagging with a cyclic dependency network
Toutanova, K., Klein, D., Manning, C. D., & Singer, Y. (2003) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.3115/1073445.1073478 · Cited by 2,889 · see citing works
Tsoumakas, G., & Katakis, I. (2007) · International Journal of Data Warehousing and Mining · doi:10.4018/jdwm.2007070101 · Cited by 2,512 · see citing works
Latent Space Approaches to Social Network Analysis
Hoff, P. D., Raftery, A. E., & Handcock, M. S. (2002) · Journal of the American Statistical Association · doi:10.1198/016214502388618906 · Cited by 1,917 · see citing works
Cao, S., Lu, W., & Xu, Q. (2015) · doi:10.1145/2806416.2806512 · Cited by 1,642 · see citing works
Hogwild: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent
Niu, F., Recht, B., Re, C., & Wright, S. J. (2011) · arXiv · doi:10.48550/arxiv.1106.5730 · Cited by 1,211 · see citing works
A large-scale evaluation of computational protein function prediction
Radivojac, P., Clark, W. T., Oron, T. R., Schnoes, A. M., Wittkop, T., Sokolov, A., Graim, K., Funk, C., Verspoor, K., Ben-Hur, A., et al. (2013) · Nature Methods · doi:10.1038/nmeth.2340 · Cited by 1,028 · see citing works
Bäckström, L., & Leskovec, J. (2011) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/1935826.1935914 · Cited by 979 · see citing works
Self-citationStanford GraphBase: A Platform for Combinatorial Computing, The
Knuth, D. E. (1993) · Cited by 917 · see citing works
The BioGRID Interaction Database: 2008 update
Breitkreutz, B., Stark, C., Reguly, T., Boucher, L., Breitkreutz, A., Livstone, M. S., Oughtred, R., Lackner, D. H., Bähler, J., Wood, V., et al. (2007) · Nucleic Acids Research · doi:10.1093/nar/gkm1001 · Cited by 698 · see citing works
Global protein function prediction from protein-protein interaction networks
Vázquez, A., Flammini, A., Maritan, A., & Vespignani, A. (2003) · Nature Biotechnology · doi:10.1038/nbt825 · Cited by 693 · see citing works
Learning Deep Representations for Graph Clustering
Tian, F., Gao, B., Cui, Q., Chen, E., & Liu, T. (2014) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v28i1.8916 · Cited by 680 · see citing works
Henderson, K. A., Gallagher, B. J., Eliassi‐Rad, T., Tong, H., Basu, S., Akoglu, L., Koutra, D., Faloutsos, C., & Li, L. (2012) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2339530.2339723 · Cited by 378 · see citing works
Graph embedding and extensions: a general framework for dimensionality reduction.
Yan, S., Xu, D., Zhang, B., et al. (2007) · PubMed · doi:10.1109/tpami.2007.12 · Cited by 376 · see citing works
Leveraging social media networks for classification
Tang, L., & Liu, H. (2011) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-010-0210-x · Cited by 360 · see citing works
Yang, S.-H., Long, B., Smola, A. J., Sadagopan, N., Zheng, Z., & Hongyuan Zha, B. (2011) · International World Wide Web Conference (WWW) · doi:10.1145/1963405.1963481 · Cited by 359 · see citing works
It's who you know: graph mining using recursive structural features
Henderson, K., Gallagher, B., Li, L., Akoglu, L., Eliassi-Rad, T., Tong, H., & Faloutsos, C. (2011) · Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2020408.2020512 · Cited by 223 · see citing works
Overlapping Communities Explain Core–Periphery Organization of Networks
Yang, J., & Leskovec, J. (2014) · Proceedings of the IEEE · doi:10.1109/jproc.2014.2364018 · Cited by 158 · see citing works
Self-citationA Deep Learning Approach to Link Prediction in Dynamic Networks
Li, X., Du, N., Li, H., Li, K., Gao, J., & Zhang, A. (2014) · SIAM International Conference on Data Mining · doi:10.1137/1.9781611973440.33 · Cited by 149 · see citing works
Gallagher, B. J., & Eliassi‐Rad, T. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-14929-0_1 · Cited by 61 · see citing works
Dropout Training of Matrix Factorization and Autoencoder for Link Prediction in Sparse Graphs
Zhai, S., & Zhang, Z. M. (2015) · SIAM International Conference on Data Mining · doi:10.1137/1.9781611974010.51 · Cited by 34 · see citing works
LRBM: A Restricted Boltzmann Machine Based Approach for Representation Learning on Linked Data
Li, K., Gao, J., Guo, S., Du, N., Xiao-yi, L., & Zhang, A. (2014) · Proceedings · doi:10.1109/icdm.2014.22 · Cited by 24 · see citing works
Flagged first, then by citations
4 of the 38 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Semi-supervised classification with graph convolutional networks
Kipf, T. N., & Welling, M. (2016) · arXiv · doi:10.48550/arxiv.1609.02907 · Cited by 24,853 · see citing works
Inductive Representation Learning on Large Graphs
Hamilton, W. L., Ying, R., & Leskovec, J. (2017) · arXiv · doi:10.48550/arxiv.1706.02216 · Cited by 10,639 · see citing works
Self-citationGraph neural networks: A review of methods and applications
Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., & Sun, M. (2020) · AI Open · doi:10.1016/j.aiopen.2021.01.001 · Cited by 6,880 · see citing works
Graph Convolutional Neural Networks for Web-Scale Recommender Systems
Ying, R., He, R., Chen, K., Eksombatchai, P., Hamilton, W. L., & Leskovec, J. (2018) · Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3219819.3219890 · Cited by 3,733 · see citing works
Self-citationVariational Graph Auto-EncodersPreprint
Kipf, T., & Welling, M. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.07308 · Cited by 3,635 · see citing works
Heterogeneous Graph Attention Network
Wang, X., Ji, H., Shi, C., Wang, B., Ye, Y., Cui, P., & Yu, P. S. (2019) · The World Wide Web Conference · doi:10.1145/3308558.3313562 · Cited by 3,191 · see citing works
A survey on semi-supervised learning
van Engelen, J. E., & Hoos, H. H. (2020) · Machine Learning · doi:10.1007/s10994-019-05855-6 · Cited by 2,666 · see citing works
Dong, Y., Chawla, N. V., & Swami, A. (2017) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3097983.3098036 · Cited by 2,224 · see citing works
Graph Neural Networks for Social Recommendation
Fan, W., Ma, Y., Li, Q., He, Y., Zhao, E., Tang, J., & Yin, D. (2019) · International World Wide Web Conference (WWW) · doi:10.1145/3308558.3313488 · Cited by 2,091 · see citing works
A Comprehensive Survey of Graph Embedding: Problems, Techniques, and Applications
Cai, H., Zheng, V. W., & Chang, K. C.-C. (2018) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2018.2807452 · Cited by 2,034 · see citing works
Session-Based Recommendation with Graph Neural Networks
Wu, S., Tang, Y., Zhu, Y., Wang, L., Xie, X., & Tan, T. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.3301346 · Cited by 1,903 · see citing works
Graph convolutional networks: a comprehensive review
Zhang, S., Tong, H., Xu, J., & Maciejewski, R. (2019) · Computational Social Networks · doi:10.1186/s40649-019-0069-y · Cited by 1,888 · see citing works
Self-supervised Learning: Generative or Contrastive
Liu, X., Zhang, F., Hou, Z., Mian, L., Wang, Z., Zhang, J., & Tang, J. (2021) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3090866 · Cited by 1,890 · see citing works
GMAN: A Graph Multi-Attention Network for Traffic Prediction
Zheng, C., Fan, X., Wang, C., & Qi, J. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i01.5477 · Cited by 1,849 · see citing works
Geometric Deep Learning on Graphs and Manifolds Using Mixture Model CNNs
Monti, F., Boscaini, D., Masci, J., Rodola, E., Svoboda, J., & Bronstein, M. M. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.576 · Cited by 1,820 · see citing works
Graph embedding techniques, applications, and performance: A survey
Goyal, P., & Ferrara, E. (2018) · Knowledge-Based Systems · doi:10.1016/j.knosys.2018.03.022 · Cited by 1,801 · see citing works
Heterogeneous Graph Neural Network
Zhang, C., Song, D., Huang, C., Swami, A., & Chawla, N. V. (2019) · Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3292500.3330961 · Cited by 1,591 · see citing works
Open Graph Benchmark: Datasets for Machine Learning on GraphsPreprint
Hu, W., Fey, M., Žitnik, M., Dong, Y., Ren, H., Liu, B., Catasta, M., & Leskovec, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.00687 · Cited by 1,439 · see citing works
Self-citationGraph Contrastive Learning with AugmentationsPreprint
You, Y., Chen, T., Sui, Y., Chen, T., Wang, Z., & Shen, Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.13902 · Cited by 1,400 · see citing works
Link Prediction Based on Graph Neural NetworksPreprint
Zhang, M., & Chen, Y. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.09691 · Cited by 1,394 · see citing works
Representation Learning on Graphs with Jumping Knowledge NetworksPreprint
Xu, K., Li, C., Tian, Y., Sonobe, T., Kawarabayashi, K., & Jegelka, S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.03536 · Cited by 1,379 · see citing works
EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs
Pareja, A., Domeniconi, G., Chen, J., Ma, T., Suzumura, T., Kanezashi, H., Kaler, T., Schardl, T. B., & Leiserson, C. E. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i04.5984 · Cited by 128 · see citing works
Cui, P., Wang, X., Pei, J., & Zhu, W. (2019) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2018.2849727 · Cited by 1,274 · see citing works
Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural NetworksPreprint
Wang, M., Zheng, D., Ye, Z., Gan, Q., Li, M., Song, X. H., Zhou, J., Ma, C., Yu, L., Gai, Y. L., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.01315 · Cited by 1,252 · see citing works
Heterogeneous Information Network Embedding for Recommendation
Shi, C., Hu, B., Zhao, W. X., & Yu, P. S. (2018) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2018.2833443 · Cited by 1,196 · see citing works
Graph Contrastive Learning with Adaptive Augmentation
Zhu, Y., Xu, Y., Yu, F., Liu, Q., Wu, S., & Wang, L. (2021) · International World Wide Web Conference (WWW) · doi:10.1145/3442381.3449802 · Cited by 1,192 · see citing works
FastGCN: Fast Learning with Graph Convolutional Networks via Importance SamplingPreprint
Chen, J., Ma, T., & Xiao, C. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1801.10247 · Cited by 1,172 · see citing works
Ribeiro, L. F. R., Saverese, P. H. P., & Figueiredo, D. R. (2017) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3097983.3098061 · Cited by 1,153 · see citing works
Strategies for Pre-training Graph Neural NetworksPreprint
Hu, W., Liu, B., Gomes, J., Žitnik, M., Liang, P., Pande, V. S., & Leskovec, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.12265 · Cited by 1,079 · see citing works
Self-citationMAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding
Fu, X., Zhang, J., Meng, Z., & King, I. (2020) · International World Wide Web Conference (WWW) · doi:10.1145/3366423.3380297 · Cited by 1,056 · see citing works
Adversarial Attacks on Neural Networks for Graph Data
Zügner, D., Akbarnejad, A., & Günnemann, S. (2018) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3219819.3220078 · Cited by 997 · see citing works
A Survey on Deep Semi-Supervised Learning
Yang, X., Song, Z., King, I., & Xu, Z. (2022) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2022.3220219 · Cited by 981 · see citing works
Community Preserving Network Embedding
Wang, X., Cui, P., Wang, J., Pei, J., Zhu, W., & Yang, S. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.10488 · Cited by 935 · see citing works
Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and GaugesPreprint
Bronstein, M. M., Bruna, J., Cohen, T., & Veličković, P. (2021) · arXiv (Cornell University) · doi:10.4230/oasics.dx.2026.1 · Cited by 928 · see citing works
Graph neural networks for materials science and chemistry
Reiser, P., Neubert, M., Eberhard, A., Torresi, L., Zhou, C., Shao, C., Metni, H., van Hoesel, C., Schopmans, H., Sommer, T., et al. (2022) · Communications Materials · doi:10.1038/s43246-022-00315-6 · Cited by 903 · see citing works
Contrastive Multi-View Representation Learning on GraphsPreprint
Hassani, K., & Khasahmadi, A. H. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.05582 · Cited by 883 · see citing works
A Comprehensive Survey on Graph Anomaly Detection With Deep Learning
Ma, X., Wu, J. X., Xue, S., Yang, J. Y., Zhou, C., Sheng, Q. Z., Xiong, H., & Akoglu, L. (2021) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3118815 · Cited by 883 · see citing works
Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification
Shi, Y., Huang, Z., Feng, S., Zhong, H., Wang, W., & Sun, Y. (2021) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2021/214 · Cited by 870 · see citing works
Graph Transformer Networks: Learning meta-path graphs to improve GNNs
Yun, S., Jeong, M., Yoo, S., Lee, S., Yi, S. S., Kim, R., Kang, J., & Kim, H. J. (2022) · Neural Networks · doi:10.1016/j.neunet.2022.05.026 · Cited by 868 · see citing works
Contrastive Representation Learning: A Framework and Review
Le-Khac, P. H., Healy, G., & Smeaton, A. F. (2020) · IEEE Access · doi:10.1109/access.2020.3031549 · Cited by 862 · see citing works
A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions
Gao, C., Zheng, Y., Li, N., Li, Y., Qin, Y., Piao, J., Quan, Y., Chang, J., Jin, D., He, X., et al. (2023) · ACM Transactions on Recommender Systems · doi:10.1145/3568022 · Cited by 854 · see citing works
Image Generation from Scene Graphs
Johnson, J., Gupta, A., & Fei-Fei, L. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00133 · Cited by 836 · see citing works
Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks
Kumar, S., Zhang, X., & Leskovec, J. (2019) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3292500.3330895 · Cited by 836 · see citing works
Self-citationQiu, J., Chen, Q., Dong, Y., Zhang, J., Yang, H., Ding, M., Wang, K., & Tang, J. (2020) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3394486.3403168 · Cited by 834 · see citing works
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Zhu, Y., Xu, Y., Yu, F., Liu, Q., Wu, S., & Wang, L. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.04131 · Cited by 784 · see citing works
Multi-Scale attributed node embedding
Rózemberczki, B., Allen, C., & Sarkar, R. (2021) · Journal of Complex Networks · doi:10.1093/comnet/cnab014 · Cited by 726 · see citing works
Network Embedding as Matrix Factorization
Qiu, J., Dong, Y., Ma, H., Li, J., Wang, K., & Tang, J. (2018) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3159652.3159706 · Cited by 726 · see citing works
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Zhang, D., Yin, J., Zhu, X., & Zhang, C. (2020) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2018.2850013 · Cited by 714 · see citing works
Leveraging Meta-path based Context for Top- N Recommendation with A Neural Co-Attention Model
Hu, B., Shi, C., Zhao, W. X., & Yu, P. S. (2018) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3219819.3219965 · Cited by 706 · see citing works
A comprehensive survey on pretrained foundation models: a history from BERT to ChatGPT
Zhou, C., Li, Q., Li, C., Yu, J., Liu, Y., Wang, G., Zhang, K., Ji, C., Yan, Q., He, L., et al. (2024) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-024-02443-6 · Cited by 704 · see citing works
GraphGAN: Graph Representation Learning With Generative Adversarial Nets
Wang, H., Wang, J., Wang, J., Zhao, M., Zhang, W., Zhang, F., Xie, X., & Guo, M. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11872 · Cited by 689 · see citing works
Hamilton, W. L. (2020) · Synthesis lectures on artificial intelligence and machine learning · doi:10.1007/978-3-031-01588-5 · Cited by 687 · see citing works
Neural Network-based Graph Embedding for Cross-Platform Binary Code Similarity Detection
Xu, X., Liu, C., Feng, Q., Yin, H., Song, L., & Song, D. (2017) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3133956.3134018 · Cited by 682 · see citing works
GraphMAE: Self-Supervised Masked Graph Autoencoders
Hou, Z., Liu, X., Cen, Y., Dong, Y., Yang, H., Wang, C., & Tang, J. (2022) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3534678.3539321 · Cited by 672 · see citing works
Poincaré Embeddings for Learning Hierarchical Representations
Nickel, M., & Kiela, D. (2017) · arXiv · doi:10.48550/arxiv.1705.08039 · Cited by 667 · see citing works
Jusup, M., Holme, P., Kanazawa, K., Takayasu, M., Romić, I., Wang, Z., Geček, S., Lipić, T., Boris Podobnik, A., Wang, L., et al. (2022) · Physics Reports · doi:10.1016/j.physrep.2021.10.005 · Cited by 662 · see citing works
Sankar, A., Wu, Y., Gou, L., Zhang, W., & Yang, H. (2020) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3336191.3371845 · Cited by 660 · see citing works
Large-Scale Learnable Graph Convolutional Networks
Gao, H., Wang, Z., & Ji, S. (2018) · Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3219819.3219947 · Cited by 638 · see citing works
graph2vec: Learning Distributed Representations of GraphsPreprint
Narayanan, A. S., Chandramohan, M., Venkatesan, R., Chen, L., Liu, Y., & Jaiswal, S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1707.05005 · Cited by 638 · see citing works
GRAM: Graph-based Attention Model for Healthcare Representation Learning
Choi, E., Bahadori, M. T., Song, L., Stewart, W. F., & Sun, J. (2017) · Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/3097983.3098126 · Cited by 637 · see citing works
Zhong, J.-X., Li, N., Kong, W., Liu, S., Li, T. H., & Li, G. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00133 · Cited by 626 · see citing works
Sun, F.-Y., Hoffmann, J., Verma, V., & Tang, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.01000 · Cited by 604 · see citing works
Fu, T.-Y., Lee, W.-C., & Lei, Z. (2017) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3132847.3132953 · Cited by 602 · see citing works
DeepInf: Social Influence Prediction with Deep Learning
Qiu, J., Tang, J., Ma, H., Dong, Y., Wang, K., & Tang, J. (2018) · Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3219819.3220077 · Cited by 587 · see citing works
Graph Representation Learning via Graphical Mutual Information Maximization
Peng, Z., Huang, W., Luo, M., Zheng, Q., Rong, Y., Xu, T., & Huang, J. (2020) · International World Wide Web Conference (WWW) · doi:10.1145/3366423.3380112 · Cited by 587 · see citing works
Membership Inference Attacks on Machine Learning: A Survey
Hu, H., Salcic, Z. A., Sun, L., Dobbie, G., Yu, P. S., & Zhang, X. (2022) · ACM Computing Surveys · doi:10.1145/3523273 · Cited by 586 · see citing works
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Xu, D., Ruan, C., Körpeoğlu, E., Kumar, S., & Achan, K. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.07962 · Cited by 565 · see citing works
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Nguyen, G. H., Lee, J. B., Rossi, R. A., Ahmed, N. K., Koh, E., & Kim, S. (2018) · International World Wide Web Conference (WWW) · doi:10.1145/3184558.3191526 · Cited by 564 · see citing works
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Benson, A. R., Abebe, R., Schaub, M. T., Jadbabaie, A., & Kleinberg, J. M. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1800683115 · Cited by 548 · see citing works
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Xian, Y., Fu, Z., Muthukrishnan, S., de Melo, G., & Zhang, Y. (2019) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3331184.3331203 · Cited by 546 · see citing works
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Veličković, P., Fedus, W., Hamilton, W. L., Lió, P., Bengio, Y., & Hjelm, R. D. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1809.10341 · Cited by 537 · see citing works
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Kumar, A., Singh, S. S., Singh, K., & Biswas, B. (2020) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2020.124289 · Cited by 525 · see citing works
Hu, Z., Dong, Y., Wang, K., Chang, K.-W., & Sun, Y. (2020) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3394486.3403237 · Cited by 517 · see citing works
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Xia, F., Sun, K., Yu, S., Aziz, A., Wan, L., Pan, S., & Liu, H. (2021) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2021.3076021 · Cited by 495 · see citing works
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Pourpanah, F., Abdar, M., Luo, Y., Zhou, X., Wang, R., Lim, C. P., Wang, X., & Wu, Q. M. J. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3191696 · Cited by 493 · see citing works
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Wang, M., Yu, L., Zheng, D., Gan, Q., Gai, Y. L., Ye, Z., Li, M., Zhou, J., Huang, Q., Ma, C., et al. (2019) · arXiv (Cornell University) · Cited by 490 · see citing works
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Wang, D., Lin, J., Cui, P., Jia, Q., Wang, Z., Fang, Y., Yu, Q., Zhou, J., Yang, S., & Qi, Y. (2019) · Proceedings · doi:10.1109/icdm.2019.00070 · Cited by 483 · see citing works
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Goyal, P., Chhetri, S. R., & Canedo, A. (2019) · Knowledge-Based Systems · doi:10.1016/j.knosys.2019.06.024 · Cited by 468 · see citing works
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Wang, X., Bo, D., Shi, C., Fan, S., Ye, Y., & Yu, P. S. (2022) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2022.3177455 · Cited by 456 · see citing works
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Zhang, Y., Chen, Q., Yang, Z., Lin, H., & Lu, Z. (2019) · Scientific Data · doi:10.1038/s41597-019-0055-0 · Cited by 455 · see citing works
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Manessi, F., Rozza, A., & Manzo, M. (2019) · Pattern Recognition · doi:10.1016/j.patcog.2019.107000 · Cited by 451 · see citing works
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Liao, L., He, X., Zhang, H., & Chua, T. (2018) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2018.2819980 · Cited by 449 · see citing works
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Ericsson, L., Gouk, H., Loy, C. C., & Hospedales, T. M. (2022) · IEEE Signal Processing Magazine · doi:10.1109/msp.2021.3134634 · Cited by 433 · see citing works
Mao, K., Zhu, J., Xiao, X., Lu, B., Wang, Z., & He, X. (2021) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3459637.3482291 · Cited by 430 · see citing works
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Liu, Z., Chen, C., Yang, X., Zhou, J., Li, X., & Song, L. (2018) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3269206.3272010 · Cited by 416 · see citing works
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Wu, J., Yuan, Q., Lin, D., You, W., Chen, W., Chen, C., & Zheng, Z. (2020) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2020.3016821 · Cited by 408 · see citing works
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Zeng, A., Shen, Z., Zhou, J., Wu, J., Fan, Y., Wang, Y., & Stanley, E. (2017) · Physics Reports · doi:10.1016/j.physrep.2017.10.001 · Cited by 406 · see citing works
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Goyal, P., Kamra, N., He, X., & Liu, Y. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.11273 · Cited by 406 · see citing works
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Li, Y., Huang, C., Ding, L., Li, Z., Pan, Y., & Gao, X. (2019) · Methods · doi:10.1016/j.ymeth.2019.04.008 · Cited by 401 · see citing works
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Cavallari, S., Zheng, V. W., Cai, H., Chang, K. C., & Cambria, E. (2017) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3132847.3132925 · Cited by 387 · see citing works
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Donnat, C. L., Žitnik, M., Hallac, D., & Leskovec, J. (2018) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3219819.3220025 · Cited by 385 · see citing works
Self-citation
- 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 · 2015: 1 · 2012: 2
- 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 ›
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