English

Equivalence between LINE and Matrix Factorization

Machine Learning 2017-11-09 v2

Abstract

LINE [1], as an efficient network embedding method, has shown its effectiveness in dealing with large-scale undirected, directed, and/or weighted networks. Particularly, it proposes to preserve both the local structure (represented by First-order Proximity) and global structure (represented by Second-order Proximity) of the network. In this study, we prove that LINE with these two proximities (LINE(1st) and LINE(2nd)) are actually factoring two different matrices separately. Specifically, LINE(1st) is factoring a matrix M (1), whose entries are the doubled Pointwise Mutual Information (PMI) of vertex pairs in undirected networks, shifted by a constant. LINE(2nd) is factoring a matrix M (2), whose entries are the PMI of vertex and context pairs in directed networks, shifted by a constant. We hope this finding would provide a basis for further extensions and generalizations of LINE.

Keywords

Cite

@article{arxiv.1707.05926,
  title  = {Equivalence between LINE and Matrix Factorization},
  author = {Qiao Wang and Zheng Wang and Xiaojun Ye},
  journal= {arXiv preprint arXiv:1707.05926},
  year   = {2017}
}

Comments

5 pages

R2 v1 2026-06-22T20:51:10.174Z