# LINE: Large-scale Information Network Embedding

@article{Tang2015LINELI,
title={LINE: Large-scale Information Network Embedding},
author={Jian Tang and Meng Qu and Mingzhe Wang and Ming Zhang and Jun Yan and Qiaozhu Mei},
journal={Proceedings of the 24th International Conference on World Wide Web},
year={2015}
}
• Published 11 March 2015
• Computer Science
• Proceedings of the 24th International Conference on World Wide Web
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. [] Key Method The method optimizes a carefully designed objective function that preserves both the local and global network structures.
3,894 Citations

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## References

SHOWING 1-10 OF 24 REFERENCES

### Distributed large-scale natural graph factorization

• Computer Science, Mathematics
WWW
• 2013
This work proposes a novel factorization technique that relies on partitioning a graph so as to minimize the number of neighboring vertices rather than edges across partitions, and decomposition is based on a streaming algorithm.

### Information network or social network?: the structure of the twitter follow graph

• Computer Science
WWW
• 2014
A characterization of the topological features of the Twitter follow graph is provided, analyzing properties such as degree distributions, connected components, shortest path lengths, clustering coefficients, and degree assortativity to hypothesize that from an individual user's perspective, Twitter starts off more like an information network, but evolves to behave more like a social network.

### Graph Embedding and Extensions: A General Framework for Dimensionality Reduction

• Computer Science
IEEE Transactions on Pattern Analysis and Machine Intelligence
• 2007
A new supervised dimensionality reduction algorithm called marginal Fisher analysis is proposed in which the intrinsic graph characterizes the intraclass compactness and connects each data point with its neighboring points of the same class, while the penalty graph connects the marginal points and characterizing the interclass separability.

### ArnetMiner: extraction and mining of academic social networks

• Computer Science
KDD
• 2008
The architecture and main features of the ArnetMiner system, which aims at extracting and mining academic social networks, are described and a unified modeling approach to simultaneously model topical aspects of papers, authors, and publication venues is proposed.

### The link prediction problem for social networks

• Computer Science
CIKM '03
• 2003
Experiments on large co-authorship networks suggest that information about future interactions can be extracted from network topology alone, and that fairly subtle measures for detecting node proximity can outperform more direct measures.

### DeepWalk: online learning of social representations

• Computer Science
KDD
• 2014
DeepWalk is an online learning algorithm which builds useful incremental results, and is trivially parallelizable, which make it suitable for a broad class of real world applications such as network classification, and anomaly detection.

### Node Classification in Social Networks

• Computer Science
Social Network Data Analytics
• 2011
When dealing with large graphs, such as those that arise in the context of online social networks, a subset of nodes may be labeled. These labels can indicate demographic values, interest, beliefs or

### Neural Word Embedding as Implicit Matrix Factorization

• Computer Science
NIPS
• 2014
It is shown that using a sparse Shifted Positive PMI word-context matrix to represent words improves results on two word similarity tasks and one of two analogy tasks, and conjecture that this stems from the weighted nature of SGNS's factorization.

### Distributed Representations of Sentences and Documents

• Computer Science
ICML
• 2014
Paragraph Vector is an unsupervised algorithm that learns fixed-length feature representations from variable-length pieces of texts, such as sentences, paragraphs, and documents, and its construction gives the algorithm the potential to overcome the weaknesses of bag-of-words models.

### Reducing the sampling complexity of topic models

• Computer Science
KDD
• 2014
An algorithm which scales linearly with the number of actually instantiated topics kd in the document, for large document collections and in structured hierarchical models kd ll k, yields an order of magnitude speedup.