The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec
Open accessAbstract
Since the invention of word2vec, the skip-gram model has significantly advanced the research of network embedding, such as the recent emergence of the DeepWalk, LINE, PTE, and node2vec approaches. In this work, we show that all of the aforementioned models with negative sampling can be unified into the matrix factorization framework with closed forms. Our analysis and proofs reveal that: (1) DeepWalk empirically produces a low-rank transformation of a network's normalized Laplacian matrix; (2) LINE, in theory, is a special case of DeepWalk when the size of vertices' context is set to one; (3) As an extension of LINE, PTE can be viewed as the joint factorization of multiple networks» Laplacians; (4) node2vec is factorizing a matrix related to the stationary distribution and transition probability tensor of a 2nd-order random walk. We further provide the theoretical connections between skip-gram based network embedding algorithms and the theory of graph Laplacian. Finally, we present the NetMF method as well as its approximation algorithm for computing network embedding. Our method offers significant improvements over DeepWalk and LINE for conventional network mining tasks. This work lays the theoretical foundation for skip-gram based network embedding methods, leading to a better understanding of latent network representation learning.
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Cites
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
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
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
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
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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
Chen, T., Wang, Z., Li, L., Li, D., Li, Z., Chang, X., Bian, P., Liang, G., Wang, Q., & Xie, T. (2025) · Schloss Dagstuhl – Leibniz-Zentrum für Informatik · doi:10.4230/lipics.ecoop.2025.6
Cited by
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
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
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
Self-citationLearning Graph Embedding With Adversarial Training Methods
Pan, S., Hu, R., Fung, S.-F., Long, G., Jiang, J., & Zhang, C. (2020) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2019.2932096 · Cited by 363
Knowledge Graph Completion: A Review
Chen, Z., Wang, Y., Zhao, B., Cheng, J., Zhao, X., & Duan, Z. (2020) · IEEE Access · doi:10.1109/access.2020.3030076 · Cited by 346
Metadata
- Authors
- Qiu, J., Dong, Y., Ma, H., Li, J., Wang, K., & Tang, J.
- Venue
- Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining
- Publisher
- ACM
- Published
- 2018-02-02
- Pages
- 459-467
- OpenAlex
- W4291474301
- Cited by
- 717
- References
- 7
- FWCI
- 36.72
- Type
- conference paper
- Language
- en
- Open access
- green
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- Qiu, J., Dong, Y., Ma, H., Li, J., Wang, K., & Tang, J. (2018). Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec. Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining. https://doi.org/10.1145/3159652.3159706
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