Line
Large-scale Information Network Embedding
Dados Bibliográficos
| ID | 23364653 |
|---|---|
| Autores | Jian Tang (0000-0002-8283-1961, Microsoft Research Asia (China)), Meng Qu (0000-0003-1938-8802, Peking University), Mingzhe Wang (0000-0001-9866-9092, Peking University), Ming Zhang (0000-0003-3023-1419, Peking University), Jun Yan (0000-0003-4560-6623, Microsoft Research Asia (China)), Qiaozhu Mei (University of Michigan) |
| Ano | 2015 |
| Páginas | 1067-1077 |
| Data de publicação | 2015-05-18 |
| Peer Reviewed | Sim |
| Open Access | Sim |
| Tipo | CONFERENCE |
| Periódico | Proceedings of the 24th International Conference on World Wide Web (CONFERENCE) |
| Editora | International World Wide Web Conferences Steering Committee (PUBLISHER) |
| DOI | 10.1145/2736277.2741093 |
| OpenAlex | W1888005072 |
| Idioma | EN |
| Citações recebidas | 73 |
| Referências citadas | 13 |
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}}.
Code (set theory) · Embedding · Function (biology) · Graph · Line (geometry) · Node (physics) · Source code · Stochastic gradient descent · Advanced Graph Neural Networks · Complex Network Analysis Techniques · Topic Modeling
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| Obras citantes distintas | 73 |
|---|---|
| Citações por ano | 7,3 |
| Intervalo de citações | 2016 - 2026 (11) |
| Velocidade de citação | current |
| Altamente citado | Não |
| Tipos de citação | Neutras: 71 |