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Enhancing Network Embedding with Auxiliary Information: An Explicit Matrix Factorization Perspective

  • Junliang Guo
  • Linli Xu
  • Xunpeng Huang
  • Enhong Chen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10827)

Abstract

Recent advances in the field of network embedding have shown the low-dimensional network representation is playing a critical role in network analysis. However, most of the existing principles of network embedding do not incorporate auxiliary information such as content and labels of nodes flexibly. In this paper, we take a matrix factorization perspective of network embedding, and incorporate structure, content and label information of the network simultaneously. For structure, we validate that the matrix we construct preserves high-order proximities of the network. Label information can be further integrated into the matrix via the process of random walk sampling to enhance the quality of embedding in an unsupervised manner, i.e., without leveraging downstream classifiers. In addition, we generalize the Skip-Gram Negative Sampling model to integrate the content of the network in a matrix factorization framework. As a consequence, network embedding can be learned in a unified framework integrating network structure and node content as well as label information simultaneously. We demonstrate the efficacy of the proposed model with the tasks of semi-supervised node classification and link prediction on a variety of real-world benchmark network datasets.

Notes

Acknowledgements

This research was supported by the National Natural Science Foundation of China (No. 61673364, No. U1605251 and No. 61727809), and the Fundamental Research Funds for the Central Universities (WK2150110008).

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Junliang Guo
    • 1
  • Linli Xu
    • 1
  • Xunpeng Huang
    • 1
  • Enhong Chen
    • 1
  1. 1.Anhui Province Key Laboratory of Big Data Analysis and Application, School of Computer Science and TechnologyUniversity of Science and Technology of ChinaHefeiChina

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