LINE: Large-scale Information Network Embedding
Open accessAbstract
This paper studies the problem of embedding very large information networks into low-dimensional vector spaces, which is useful in many tasks such as visualization, node classification, and link prediction. Most existing graph embedding methods do not scale for real world information networks which usually contain millions of nodes. In this paper, we propose a novel network embedding method called the ``LINE,'' which is suitable for arbitrary types of information networks: undirected, directed, and/or weighted. The method optimizes a carefully designed objective function that preserves both the local and global network structures. An edge-sampling algorithm is proposed that addresses the limitation of the classical stochastic gradient descent and improves both the effectiveness and the efficiency of the inference. Empirical experiments prove the effectiveness of the LINE on a variety of real-world information networks, including language networks, social networks, and citation networks. The algorithm is very efficient, which is able to learn the embedding of a network with millions of vertices and billions of edges in a few hours on a typical single machine. The source code of the LINE is available online\footnote{\url{https://github.com/tangjianpku/LINE}}.
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- Authors
- Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., & Mei, Q.
- Affiliations
- Microsoft Research Asia (China) (CN)
- Peking University (CN)
- University of Michigan (US)
- ORCID
- Venue
- Proceedings of the 24th International Conference on World Wide Web
- Published
- 2015-05-18
- Pages
- 1067-1077
- OpenAlex
- W1888005072
- Cited by
- 5,416
- References
- 18
- Citations by year
- 2026: 155 · 2025: 322 · 2024: 429 · 2023: 567 · 2022: 653 · 2021: 917 · 2020: 835 · 2019: 774 · 2018: 477 · 2017: 222 · 2016: 59 · 2015: 4
- FWCI
- 143.11
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- International World Wide Web Conference (WWW) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (IIS-1054199, CCF-1048168)
- National Natural Science Foundation of China (61472006)
- 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
- lines, graph embedding, edge sampling, stochastic gradient descent, node classification, link prediction, network visualization, social networks, citation networks, language network
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