Network Representation Learning: A Survey
Open accessAbstract
With the widespread use of information technologies, information networks are becoming increasingly popular to capture complex relationships across various disciplines, such as social networks, citation networks, telecommunication networks, and biological networks. Analyzing these networks sheds light on different aspects of social life such as the structure of societies, information diffusion, and communication patterns. In reality, however, the large scale of information networks often makes network analytic tasks computationally expensive or intractable. Network representation learning has been recently proposed as a new learning paradigm to embed network vertices into a low-dimensional vector space, by preserving network topology structure, vertex content, and other side information. This facilitates the original network to be easily handled in the new vector space for further analysis. In this survey, we perform a comprehensive review of the current literature on network representation learning in the data mining and machine learning field. We propose new taxonomies to categorize and summarize the state-of-the-art network representation learning techniques according to the underlying learning mechanisms, the network information intended to preserve, as well as the algorithmic designs and methodologies. We summarize evaluation protocols used for validating network representation learning including published benchmark datasets, evaluation methods, and open source algorithms. We also perform empirical studies to compare the performance of representative algorithms on common datasets, and analyze their computational complexity. Finally, we suggest promising research directions to facilitate future study.
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(2008) · Journal of Machine Learning Research · Cited by 33,051 · 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
Distributed Representations of Words and Phrases and their Compositionality
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1310.4546 · Cited by 24,533 · see citing works
Labaca-Castro, R. (2023) · Springer eBooks · doi:10.1007/978-3-658-40442-0_9 · Cited by 21,100 · see citing works
Reducing the dimensionality of data with neural networks
Hinton, G. E., & Salakhutdinov, R. R. (2006) · Science · doi:10.1126/science.1127647 · Cited by 20,928 · see citing works
GAN（Generative Adversarial Nets）
淳司, 柴. (2017) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.29.5_177_2 · Cited by 19,268 · see citing works
Normalized cuts and image segmentation
Shi, J., & Malik, J. (2000) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.868688 · Cited by 15,842 · see citing works
Community structure in social and biological networks
Girvan, M., & Newman, M. E. J. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.122653799 · Cited by 15,502 · 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
Finding and evaluating community structure in networks
Newman, M. E. J., & Girvan, M. (2004) · Physical Review E · doi:10.1103/physreve.69.026113 · Cited by 13,931 · 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
Modularity and community structure in networks
Newman, M. E. J. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0601602103 · Cited by 12,256 · see citing works
Fortunato, S. (2010) · Physics Reports · doi:10.1016/j.physrep.2009.11.002 · Cited by 11,293 · see citing works
node2vec: Scalable Feature Learning for Networks
Grover, A., & Leskovec, J. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939754 · Cited by 10,926 · see citing works
Distributed Representations of Sentences and DocumentsPreprint
Le, Q. V., & Mikolov, T. (2014) · International Conference on Machine Learning · doi:10.48550/arxiv.1405.4053 · Cited by 10,307 · 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
LIBLINEAR: A Library for Large Linear Classification
Fan, R.-E., Chang, K., Hsieh, C., Xiang-rui, W., & Lin, C. (2008) · doi:10.5555/1390681.1442794 · Cited by 6,680 · see citing works
Improving neural networks by preventing co-adaptation of feature detectors
Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. R. (2012) · arXiv · doi:10.48550/arxiv.1207.0580 · Cited by 6,382 · 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
Targeted Branching for the Maximum Independent Set Problem Using Graph Neural Networks
Silva, G., Rodrigues, M., Teixeira, A., et al. (2024) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.sea.2024.20 · Cited by 5,266 · see citing works
Finding community structure in networks using the eigenvectors of matrices
Newman, M. E. J. (2006) · Physical Review E · doi:10.1103/physreve.74.036104 · Cited by 4,799 · 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
(2010) · Journal of Machine Learning Research · doi:10.5555/1756006.1953039 · Cited by 4,051 · see citing works
A New Status Index Derived from Sociometric Analysis
Katz, L. (1953) · Psychometrika · doi:10.1007/bf02289026 · Cited by 3,945 · see citing works
Learning Entity and Relation Embeddings for Knowledge Graph Completion
Lin, Y., Liu, Z., Sun, M., Liu, Y., & Zhu, X. (2015) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v29i1.9491 · Cited by 3,749 · see citing works
Self-citationGeometric Deep Learning: Going beyond Euclidean data
Bronstein, M. M., Bruna, J., LeCun, Y., Szlam, A., & Vandergheynst, P. (2017) · IEEE Signal Processing Magazine · doi:10.1109/msp.2017.2693418 · Cited by 3,661 · see citing works
Collective Classification in Network Data
Sen, P., Namata, G., Bilgic, M., Getoor, L., Gallagher, B., & Eliassi‐Rad, T. (2008) · AI Magazine · doi:10.1609/aimag.v29i3.2157 · Cited by 3,293 · see citing works
The link‐prediction problem for social networks
Liben‐Nowell, D., & Kleinberg, J. M. (2007) · Journal of the American Society for Information Science and Technology · doi:10.1002/asi.20591 · Cited by 3,063 · see citing works
Structural Deep Network Embedding
Wang, D., Cui, P., & Zhu, W. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939753 · Cited by 2,845 · see citing works
Link prediction in complex networks: A survey
Lü, L., & Zhou, T. (2010) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2010.11.027 · Cited by 2,841 · see citing works
Random Features for Large-Scale Kernel Machines
Rahimi, A., & Recht, B. (2007) · Cited by 2,783 · see citing works
The political blogosphere and the 2004 U.S. election: divided they blog
Adamic, L. A., & Glance, N. (2005) · Proceedings of the 3rd international workshop on Link discovery · doi:10.1145/1134271.1134277 · Cited by 2,619 · see citing works
Leskovec, J., Kleinberg, J. M., & Faloutsos, C. (2007) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/1217299.1217301 · Cited by 2,494 · 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
Tang, J., Zhang, J., Yao, L., Li, J., Zhang, L., & Su, Z. (2008) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1401890.1402008 · Cited by 2,156 · 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
Mining interesting locations and travel sequences from GPS trajectories
Zheng, Y., Zhang, L., Xie, X., & Ma, W. (2009) · International World Wide Web Conference (WWW) · doi:10.1145/1526709.1526816 · Cited by 1,957 · 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
Cao, S., Lu, W., & Xu, Q. (2015) · doi:10.1145/2806416.2806512 · Cited by 1,642 · see citing works
Graph visualization and navigation in information visualization: A survey
Herman, I., Mélançon, G., & Marshall, M. S. (2000) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/2945.841119 · Cited by 1,559 · see citing works
Asymmetric Transitivity Preserving Graph Embedding
Ou, M., Cui, P., Pei, J., Zhang, Z., & Zhu, W. (2016) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2939672.2939751 · Cited by 1,298 · 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
Salakhutdinov, R., & Hinton, G. (2009) · International Journal of Approximate Reasoning · doi:10.1016/j.ijar.2008.11.006 · Cited by 1,246 · 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
Deep Neural Networks for Learning Graph Representations
Cao, S., Lu, W., & Xu, Q. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10179 · Cited by 1,070 · see citing works
Supervised Dictionary LearningPreprint
Mairal, J., Bach, F., Ponce, J., Sapiro, G., & Zisserman, A. (2008) · arXiv (Cornell University) · doi:10.48550/arxiv.0809.3083 · Cited by 996 · see citing works
Network representation learning with rich text information
Yang, C. H., Liu, Z., Zhao, D., Sun, M., & Chang, E. Y. (2015) · Cited by 977 · 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
RhizoDB: A Bounded Activation-Flow Architecture for Graph-Based Memory Systems
Guberte, J. A. (2026) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.20695702 · Cited by 897 · see citing works
A min-max cut algorithm for graph partitioning and data clustering
Ding, C. H. Q., He, X., Hongyuan Zha, B., Gu, M., & Simon, H. D. (2002) · Proceedings · doi:10.1109/icdm.2001.989507 · Cited by 850 · see citing works
Learning Structured Embeddings of Knowledge Bases
Bordes, A., Weston, J., Collobert, R., & Bengio, Y. (2011) · AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) · doi:10.1609/aaai.v25i1.7917 · Cited by 849 · see citing works
A Scalable Hierarchical Distributed Language Model
Mnih, A., & Hinton, G. E. (2008) · Cited by 843 · see citing works
Xu, X., Yuruk, N., Feng, Z., & Schweiger, T. A. J. (2007) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1281192.1281280 · Cited by 841 · see citing works
(2005) · Cited by 823 · see citing works
Relational learning via latent social dimensions
Tang, L., & Liu, H. (2009) · Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1557019.1557109 · Cited by 799 · 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
Social structure of Facebook networks
Traud, A. L., Mucha, P. J., & Porter, M. A. (2011) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2011.12.021 · Cited by 721 · see citing works
A Survey of Link Prediction in Complex Networks
Martínez, V., Berzal, F., & Cubero, J.-C. (2016) · ACM Computing Surveys · doi:10.1145/3012704 · Cited by 700 · 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
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
Clustering and Community Detection in Directed Networks: A SurveyPreprint
Malliaros, F. D., & Vazirgiannis, M. (2016) · Cited by 637 · see citing works
Heterogeneous Network Embedding via Deep Architectures
Chang, S., Han, W., Tang, J., Qi, G.-J., Aggarwal, C. C., & Huang, T. S. (2015) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2783258.2783296 · Cited by 602 · 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
Gutmann, M. U., & Hyvärinen, A. (2012) · Edinburgh Research Explorer (University of Edinburgh) · doi:10.5555/2503308.2188396 · Cited by 596 · see citing works
Clustering and community detection in directed networks: A survey
Malliaros, F. D., & Vazirgiannis, M. (2013) · Physics Reports · doi:10.1016/j.physrep.2013.08.002 · Cited by 594 · see citing works
NODE CLASSIFICATION IN SOCIAL NETWORKS
Bhagat, S., Cormode, G., & Muthukrishnan, S. (2011) · Cited by 500 · see citing works
Label Informed Attributed Network Embedding
Huang, X. X., Li, J., & Hu, X. (2017) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3018661.3018667 · Cited by 498 · see citing works
Dynamic Network Embedding by Modeling Triadic Closure Process
Zhou, L.-K., Yang, Y., Ren, X., Wu, F. Q., & Zhuang, Y. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11257 · Cited by 484 · see citing works
Wang, C., Pan, S., Long, G., Xingquan Zhu, A., & Jiang, J. B. (2017) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3132847.3132967 · Cited by 419 · see citing works
Attributed Network Embedding for Learning in a Dynamic Environment
Li, J., Dani, H., Hu, X., Tang, J.-L., Chang, Y., & Liu, H. (2017) · International Conference on Information and Knowledge Management · doi:10.1145/3132847.3132919 · Cited by 399 · see citing works
Learning Graph-based POI Embedding for Location-based Recommendation
Xie, M., Yin, H., Wang, H., Xu, F., Chen, W., & Wang, S. (2016) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/2983323.2983711 · Cited by 397 · see citing works
Visualizing Large-scale and High-dimensional Data
Tang, J., Liu, J., Zhang, M., & Mei, Q. (2016) · International World Wide Web Conference (WWW) · doi:10.1145/2872427.2883041 · Cited by 395 · see citing works
Node Classification in Social Networks
Bhagat, S., Cormode, G., & Muthukrishnan, S. (2011) · Springer eBooks · doi:10.1007/978-1-4419-8462-3_5 · Cited by 391 · see citing works
Learning Community Embedding with Community Detection and Node Embedding on Graphs
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
Tri-party deep network representation
Pan, S., Wu, J. X., Xingquan Zhu, A., Zhang, C., & Wang, Y. (2016) · Griffith Research Online · Cited by 376 · see citing works
Self-citationHARP: Hierarchical Representation Learning for Networks
Chen, H., Perozzi, B., Hu, Y., & Skiena, S. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11849 · Cited by 367 · 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
Wang, H., Zhang, F., Hou, M., Xie, X., Guo, M., & Liu, Q. (2018) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3159652.3159666 · Cited by 356 · see citing works
Inductive matrix completion for predicting gene–disease associations
Natarajan, N., & Dhillon, I. S. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu269 · Cited by 311 · see citing works
Combining content and link for classification using matrix factorization
Shenghuo Zhu, B., Yu, K., Chi, Y., & Gong, Y. (2007) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/1277741.1277825 · Cited by 264 · see citing works
Signed Network Embedding in Social Media
Wang, S., Tang, J., Aggarwal, C. C., Chang, Y., & Liu, H. (2017) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9781611974973.37 · Cited by 255 · see citing works
Max-margin deepwalk: discriminative learning of network representation
Tu, C., Zhang, W., Liu, Z., & Sun, M. (2016) · International Joint Conference on Artificial Intelligence · Cited by 250 · see citing works
Scalable learning of collective behavior based on sparse social dimensions
Tang, L., & Liu, H. (2009) · Proceedings of the 18th ACM conference on Information and knowledge management · doi:10.1145/1645953.1646094 · Cited by 233 · see citing works
Structural Deep Embedding for Hyper-Networks
Tu, K., Cui, P., Wang, X., Wang, F., & Zhu, W. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11266 · Cited by 227 · see citing works
Dai, Q., Li, Q., Tang, J., & Wang, D. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11865 · Cited by 214 · see citing works
Scalable Graph Embedding for Asymmetric Proximity
Zhou, C., Liu, Y., Liu, X., Liu, Z., & Gao, J. Y. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.10878 · Cited by 206 · see citing works
MedLDA: Maximum Margin Supervised Topic Models
Zhu, J., Ahmed, A. A., & Xing, E. P. (2018) · Figshare · doi:10.1184/r1/6475874 · Cited by 205 · see citing works
Scalable proximity estimation and link prediction in online social networks
Song, H. H., Cho, T. W., Dave, V., Zhang⋆, Y., & Qiu, L. (2009) · ACM Internet Measurement Conference (IMC) · doi:10.1145/1644893.1644932 · Cited by 181 · see citing works
An Attention-based Collaboration Framework for Multi-View Network Representation Learning
Qu, M., Tang, J., Shang, J., Ren, X., Zhang, M., & Han, J. (2017) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3132847.3133021 · Cited by 172 · see citing works
Temporal link prediction by integrating content and structure information
Gao, S., Denoyer, L., & Gallinari, P. (2011) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/2063576.2063744 · Cited by 163 · see citing works
Chebyshev polynomial approximation for distributed signal processing
Shuman, D. I., Vandergheynst, P., & Frossard, P. (2011) · doi:10.1109/dcoss.2011.5982158 · Cited by 154 · see citing works
Discriminative Deep Random Walk for Network Classification
Li, J., Zhu, J., & Zhang, B. (2016) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p16-1095 · Cited by 142 · see citing works
Zhang, C., Zhang, K., Yuan, Q., Peng, H., Zheng, Y., Hanratty, T., Wang, S., & Han, J. (2017) · International World Wide Web Conference (WWW) · doi:10.1145/3038912.3052601 · Cited by 142 · see citing works
Self-citationAdversarially 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 · see citing works
Self-citationHARP: Hierarchical Representation Learning for NetworksPreprint
Chen, H., Perozzi, B., Hu, Y., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.07845 · Cited by 124 · see citing works
Attributed Signed Network Embedding
Wang, S., Aggarwal, C. C., Tang, J., et al. (2017) · doi:10.1145/3132847.3132905 · Cited by 118 · see citing works
Homophily, Structure, and Content Augmented Network Representation Learning
Zhang, D., Yin, J., Xingquan Zhu, A., & Zhang, C. (2016) · Proceedings · doi:10.1109/icdm.2016.0072 · Cited by 112 · see citing works
Self-citationMulti-Dimensional Network Embedding with Hierarchical Structure
Ma, Y., Ren, Z., Jiang, Z., Tang, J., & Yin, D. (2018) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3159652.3159680 · Cited by 100 · see citing works
Heterogeneous Information Network Embedding for Meta Path based ProximityPreprint
Huang, Z., & Mamoulis, N. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1701.05291 · Cited by 99 · see citing works
Linked Document Embedding for Classification
Wang, S., Tang, J., Aggarwal, C. C., & Liu, H. (2016) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/2983323.2983755 · Cited by 91 · see citing works
User Profile Preserving Social Network Embedding
Zhang, D., Yin, J., Xingquan Zhu, A., & Zhang, C. (2017) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2017/472 · Cited by 86 · see citing works
Self-citationPPNE: Property Preserving Network Embedding
Li, C., Wang, S., Yang, D., Li, Z., Yang, Y., Zhang, X., & Zhou, J. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-55753-3_11 · Cited by 77 · see citing works
Djuric, N., Wu, H., Radosavljević, V., et al. (2015) · International World Wide Web Conference (WWW) · doi:10.1145/2736277.2741643 · Cited by 75 · see citing works
GAKE: Graph Aware Knowledge Embedding
Feng, J., Huang, M., Yang, Y., et al. (2016) · International Conference on Computational Linguistics · Cited by 75 · see citing works
Structural Deep Embedding for Hyper-NetworksPreprint
Tu, K., Cui, P., Wang, X., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.10146 · Cited by 71 · see citing works
Incorporate Group Information to Enhance Network Embedding
Chen, J., Zhang, Q., & Huang, X. (2016) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/2983323.2983869 · Cited by 70 · see citing works
COSINE: Community-Preserving Social Network Embedding From Information Diffusion Cascades
Zhang, Y., Lyu, T., & Zhang, Y. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11856 · Cited by 67 · see citing works
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 · see citing works
Hu, J., Cheng, R. C. K., Huang, Z., Fang, Y., & Luo, S. (2017) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3132847.3132885 · Cited by 59 · see citing works
Enhancing the Network Embedding Quality with Structural Similarity
Lyu, T., Zhang, Y., & Zhang, Y. (2017) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3132847.3132900 · Cited by 59 · see citing works
Representation Learning for Scale-Free Networks
Feng, R., Yang, Y., Hu, W., et al. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11256 · Cited by 59 · see citing works
Learning Node Embeddings in Interaction Graphs
Zhang, Y., Xiong, Y., Kong, X., & Zhu, Y. (2017) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3132847.3132918 · Cited by 58 · see citing works
Yang, D., Wang, S., Li, C., Zhang, X., & Li, Z. (2017) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3132847.3132975 · Cited by 54 · see citing works
Label-dependent node classification in the network
Kazienko, P., & Kajdanowicz, T. J. (2011) · Neurocomputing · doi:10.1016/j.neucom.2011.04.047 · Cited by 48 · see citing works
Exploring Expert Cognition for Attributed Network Embedding
Huang, X. X., Song, Q., Li, J., & Hu, X. (2018) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3159652.3159655 · Cited by 42 · see citing works
Distance-Aware DAG Embedding for Proximity Search on Heterogeneous Graphs
Liu, Z., Zheng, V. W., Zhao, Z. L., et al. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11885 · Cited by 40 · see citing works
Collective Classification via Discriminative Matrix Factorization on Sparsely Labeled Networks
Zhang, D., Yin, J., Xingquan Zhu, A., & Zhang, C. (2016) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/2983323.2983754 · Cited by 39 · see citing works
Self-citationWang, Z., Ye, X., Wang, C., Wu, Y., Wang, C., & Liang, K. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11242 · Cited by 35 · see citing works
A Jacobi–Davidson type method for the generalized singular value problem
Hochstenbach, M. E. (2009) · Linear Algebra and its Applications · doi:10.1016/j.laa.2009.03.003 · Cited by 31 · see citing works
Semi-Supervised Network Embedding
Li, C., Li, Z., Wang, S., Yang, Y., Zhang, X., & Zhou, J. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-55753-3_9 · Cited by 31 · see citing works
Deep Feature Learning for GraphsPreprint
Rossi, R. A., Zhou, R., & Ahmed, N. K. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1704.08829 · Cited by 29 · see citing works
Paired Restricted Boltzmann Machine for Linked Data
Wang, S., Tang, J., Morstatter, F., & Liu, H. (2016) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/2983323.2983756 · Cited by 24 · see citing works
Learning network representations
Moyano, L. G. (2017) · The European Physical Journal Special Topics · doi:10.1140/epjst/e2016-60266-2 · Cited by 24 · see citing works
HINE: Heterogeneous Information Network Embedding
Chen, Y., & Wang, C. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-55753-3_12 · Cited by 24 · see citing works
Spectral Graph Wavelets for Structural Role Similarity in NetworksPreprint
Donnat, C., Žitnik, M., Hallac, D., et al. (2018) · arXiv (Cornell University) · Cited by 20 · see citing works
Multi-Facet Network Embedding: Beyond the General Solution of Detection and Representation
Yang, L., Guo, Y., & Cao, X. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11247 · Cited by 19 · see citing works
TLINE: Scalable Transductive Network Embedding
Zhang, X., Chen, W., & Yan, H. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-48051-0_8 · Cited by 13 · see citing works
Bernoulli Embeddings for Graphs
Misra, V., & Bhatia, S. (2018) · AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) · doi:10.1609/aaai.v32i1.11733 · Cited by 9 · see citing works
Ensemble-Spotting: Prioritizing Vibrant Communities Via POI Embedding with Multi-View Spatial Graphs
Wang, P., Zhang, J., Liu, G., et al. (2018) · Cited by 1 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
A Comprehensive Survey on Graph Neural Networks
Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., & Yu, P. S. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.2978386 · Cited by 11,000 · 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
Adversarially Regularized Graph Autoencoder for Graph Embedding
Pan, S., Hu, R., Long, G., Jiang, J., Yao, L., & Zhang, C. (2018) · Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/362 · Cited by 929 · see citing works
Self-citationTraffic Flow Prediction via Spatial Temporal Graph Neural Network
Wang, X., Ma, Y., Wang, Y., Jin, W., Wang, X., Tang, J., Jia, C., & Yu, J. (2020) · Proceedings of The Web Conference 2020 · doi:10.1145/3366423.3380186 · Cited by 644 · see citing works
DPA-2: a large atomic model as a multi-task learner
Zhang, D., Liu, X., Zhang, X., Zhang, C., Cai, C., Bi, H., Du, Y., Qin, X., Peng, A., Huang, J., et al. (2023) · arXiv · doi:10.48550/arxiv.2312.15492 · Cited by 155
Self-citationApplications 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 · see citing works
AvgNet: Adaptive Visibility Graph Neural Network and Its Application in Modulation Classification
Xuan, Q., Zhou, J., Qiu, K., Chen, Z., Xu, D., Zheng, S., & Yang, X. (2022) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2022.3146836 · Cited by 106 · see citing works
“It is worth noting that, due to the great success of GNN [34], [35], [36], [37] and effective graph mapping methods, it would be also an exciting direction to first map time series into graphs and then utilize powerful GNN to classify them.”
BackgroundUni-Mol: A Universal 3D Molecular Representation Learning Framework
Zhou, G., Gao, Z., Ding, Q., Zheng, H., Xu, H., Wei, Z., Zhang, L., & Ke, G. (2022) · doi:10.26434/chemrxiv-2022-jjm0j · Cited by 81 · see citing works
Wang, R., Liu, Z., Pang, J., Sun, J., Xiang, M., & Ning, X. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3579060 · Cited by 9 · see citing works
- Authors
- Zhang, D., Yin, J., Zhu, X., & Zhang, C.
- Affiliations
- University of Technology Sydney (AU)
- The University of Sydney (AU)
- Venue
- IEEE Transactions on Big Data
- Published
- 2020-03-01
- Volume
- 6
- Issue
- 1
- Pages
- 3-28
- ISSN
- 2332-7790 (ISSN-L)
- 2372-2096
- OpenAlex
- W2963512530
- Cited by
- 714
- References
- 130
- Citations by year
- 2026: 33 · 2025: 72 · 2024: 74 · 2023: 110 · 2022: 117 · 2021: 115 · 2020: 108 · 2019: 68 · 2018: 17
- FWCI
- 43.39
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Transactions on Big Data (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- UTS ePRESS (University of Technology Sydney) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (IIS-1763452)
- Australian Research Council (LP160100630, DP180100966)
- China Scholarship Council (201506300082)
- Data61
- Commonwealth Scientific and Industrial Research Organisation
- 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 › Computer Vision and Pattern Recognition › Graph Theory and Algorithms
- Keywords
- network representation learning, node embeddings, information networks, social networks, citation networks, biological networks, network topology preservation, side information, benchmark dataset, evaluation protocol, computational complexity
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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