Abstract
Many Network Representation Learning (NRL)
methods have been proposed to learn vector representations for vertices in a network recently. In this
paper, we summarize most existing NRL methods
into a unified two-step framework, including proximity matrix construction and dimension reduction.
We focus on the analysis of proximity matrix construction step and conclude that an NRL method
can be improved by exploring higher order proximities when building the proximity matrix. We propose Network Embedding Update (NEU) algorithm
which implicitly approximates higher order proximities with theoretical approximation bound and
can be applied on any NRL methods to enhance
their performances. We conduct experiments on
multi-label classification and link prediction tasks.
Experimental results show that NEU can make a
consistent and significant improvement over a number of NRL methods with almost negligible running time on all three publicly available datasets.
The source code of this paper can be obtained from
https://github.com/thunlp/NEU