Structural Deep Network Embedding
Abstract
Network embedding is an important method to learn low-dimensional representations of vertexes in networks, aiming to capture and preserve the network structure. Almost all the existing network embedding methods adopt shallow models. However, since the underlying network structure is complex, shallow models cannot capture the highly non-linear network structure, resulting in sub-optimal network representations. Therefore, how to find a method that is able to effectively capture the highly non-linear network structure and preserve the global and local structure is an open yet important problem. To solve this problem, in this paper we propose a Structural Deep Network Embedding method, namely SDNE. More specifically, we first propose a semi-supervised deep model, which has multiple layers of non-linear functions, thereby being able to capture the highly non-linear network structure. Then we propose to exploit the first-order and second-order proximity jointly to preserve the network structure. The second-order proximity is used by the unsupervised component to capture the global network structure. While the first-order proximity is used as the supervised information in the supervised component to preserve the local network structure. By jointly optimizing them in the semi-supervised deep model, our method can preserve both the local and global network structure and is robust to sparse networks. Empirically, we conduct the experiments on five real-world networks, including a language network, a citation network and three social networks. The results show that compared to the baselines, our method can reconstruct the original network significantly better and achieves substantial gains in three applications, i.e. multi-label classification, link prediction and visualization.
Integrity signals
Registry
Cites
Statistics
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?
DNGR and SDNE only consider node structural information and ignore node feature information.
“DNGR [59] and SDNE [60] only consider node structural information which is about the connectivity between pairs of nodes. They ignore nodes may contain feature information that depicts the attributes of nodes themselves.”
From A Comprehensive Survey on Graph Neural Networks (2021) · cites this paper as [60]
Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works
(2008) · Journal of Machine Learning Research · Cited by 33,051 · 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
A fast learning algorithm for deep belief nets
Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006) · Neural Computation · doi:10.1162/neco.2006.18.7.1527 · Cited by 16,336 · 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
Advances in Neural Information Processing Systems 25
Zhang, Y., Sutton, C. A., Storkey, A., & Zoubin Ghahramani, A. (2012) · Cited by 12,653 · see citing works
Hinton, G., Deng, L., Yu, D., Dahl, G., Mohamed, A.-R., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T., et al. (2012) · IEEE Signal Processing Magazine · doi:10.1109/msp.2012.2205597 · Cited by 10,260 · 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
On Spectral Clustering: Analysis and an algorithm
Ng, A. Y., Jordan, M. I., & Weiss, Y. (2001) · Cited by 8,159 · see citing works
Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
Belkin, M., & Niyogi, P. (2003) · Neural Computation · doi:10.1162/089976603321780317 · Cited by 7,562 · see citing works
Learning Deep Architectures for AI
Bengio, Y. (2009) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000006 · Cited by 7,048 · see citing works
Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank
Socher, R., Perelygin, A., Wu, J., Chuang, J., Manning, C. D., Ng, A., & Potts, C. (2013) · Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d13-1170 · Cited by 6,878 · 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
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
Learning Deep Architectures for AI
Bengio, Y. (2009) · now publishers, Inc. eBooks · doi:10.1561/9781601982957 · Cited by 5,037 · see citing works
Advances in Neural Information Processing Systems 20
Bonilla, E. V., Chai, K. M. A., & Williams, C. K. I. (2008) · neural information processing systems · Cited by 2,701 · see citing works
Why Does Unsupervised Pre-training Help Deep Learning?
Erhan, D., Bengio, Y., Courville, A. C., Manzagol, P.-A., Vincent, P., & Bengio, S. (2010) · International Conference on Artificial Intelligence and Statistics · doi:10.5555/1756006.1756025 · Cited by 2,579 · 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
Weiss, Y., Torralba, A. B., & Fergus, R. (2008) · neural information processing systems · Cited by 2,389 · see citing works
Proceedings of the 29th International Conference on Machine Learning (ICML-12)Preprint
Langford, J. C., & Pineau, J. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1207.4676 · Cited by 1,687 · see citing works
A matrix factorization technique with trust propagation for recommendation in social networks
Jamali, M., & Ester, M. (2010) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/1864708.1864736 · Cited by 1,662 · see citing works
Cao, S., Lu, W., & Xu, Q. (2015) · doi:10.1145/2806416.2806512 · Cited by 1,642 · 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
Vishwanathan, S. V. N., Schraudolph, N. N., Kondor, R., & Borgwardt, K. M. (2010) · Cited by 840 · 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
Classification with Noisy Labels by Importance Reweighting
Liu, T., & Tao, D. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2456899 · Cited by 796 · 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
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
Structure of growing social networks
Jin, E., Girvan, M., & Newman, M. E. J. (2001) · Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics · doi:10.1103/physreve.64.046132 · Cited by 508 · see citing works
Multi-View Intact Space Learning
Xu, C., Tao, D., & Xu, C. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2417578 · Cited by 395 · 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
Structure preserving embedding
Shaw, B. J., & Jebara, T. (2009) · International Conference on Machine Learning · doi:10.1145/1553374.1553494 · Cited by 211 · see citing works
A non-IID Framework for Collaborative Filtering with Restricted Boltzmann Machines
Georgiev, K. B., & Nakov, P. (2013) · Cited by 132 · see citing works
Deep multimodal hashing with orthogonal regularization
Wang, D., Cui, P., Ou, M., & Zhu, W. (2015) · International Conference on Artificial Intelligence · Cited by 111 · see citing works
Self-citationTwo-layer multiple kernel learning
Zhuang, J., Tsang, I. W.-H., & Hoi, S. C. H. (2011) · Cited by 92 · see citing works
Luo, D., Nie, F., Huang, H., et al. (2011) · Cited by 76 · 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
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
Self-citationA 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
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
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
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
Self-citationAdversarially 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
Network Representation Learning: A Survey
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
Learning Deep Network Representations with Adversarially Regularized Autoencoders
Yu, W., Zheng, C., Cheng, W., Aggarwal, C. C., Song, D., Zong, B., Chen, H., & Wang, W. (2018) · Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3219819.3220000 · Cited by 188 · see citing works
Deep Recursive Network Embedding with Regular Equivalence
Tu, K., Cui, P., Wang, X., Yu, P. S., & Zhu, W. (2018) · Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3219819.3220068 · Cited by 179 · see citing works
Self-citationDisease Prediction via Graph Neural Networks
Sun, Z., Yin, H., Chen, H., Chen, T., Cui, L., & Yang, F. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.3004143 · Cited by 98 · see citing works
Tsuji, S., Hase, T., Yachie-Kinoshita, A., Nishino, T., Ghosh, S., Kikuchi, M., Shimokawa, K., Aburatani, H., Kitano, H., & Tanaka, H. (2021) · Alzheimer's Research & Therapy · doi:10.1186/s13195-021-00826-3 · Cited by 75 · see citing works
Shen, X., Pan, S., Liu, W., Ong, Y.-S., & Sun, Q.-S. (2018) · Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/493 · Cited by 57 · see citing works
Harmonized representation learning on dynamic EHR graphs
Lee, D., Jiang, X., & Yu, H. (2020) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2020.103426 · Cited by 34 · see citing works
Yao, H., Guan, J., & Liu, T. (2020) · Journal of Bioinformatics and Computational Biology · doi:10.1142/s0219720020400107 · Cited by 23 · see citing works
Informer: irregular traffic detection for containerized microservices RPC in the real world
Chen, J., Huang, H., & Chen, H. (2019) · Proceedings of the 4th ACM/IEEE Symposium on Edge Computing · doi:10.1145/3318216.3363375 · Cited by 6 · see citing works
- Authors
- Wang, D., Cui, P., & Zhu, W.
- Affiliations
- Tsinghua University (CN)
- Venue
- Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
- Publisher
- ACM
- Published
- 2016-08-13
- Pages
- 1225-1234
- OpenAlex
- W2393319904
- Cited by
- 2,845
- References
- 39
- Citations by year
- 2026: 63 · 2025: 154 · 2024: 236 · 2023: 329 · 2022: 389 · 2021: 468 · 2020: 485 · 2019: 390 · 2018: 242 · 2017: 83 · 2016: 5
- FWCI
- 129.32
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) (publishedVersion)
- Funders
- National Natural Science Foundation of China (6137002 61531006 61472444 61210008)
- National Program on Key Basic Research Project (2015CB352300)
- 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 › Face and Expression Recognition
- Keywords
- graph embedding, semi-supervised deep learning, sparse networks, network inference, multi-label classification, link prediction, network visualization
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
