  Graph embedding methods aim at finding useful graph representations by
mapping nodes to a low-dimensional vector space. It is a task with important
downstream applications, such as link prediction, graph reconstruction, data
visualization, node classification, and language modeling. In recent years, the
field of graph embedding has witnessed a shift from linear algebraic approaches
towards local, gradient-based optimization methods combined with random walks
and deep neural networks to tackle the problem of embedding large graphs.
However, despite this improvement in the optimization tools, graph embedding
methods are still generically designed in a way that is oblivious to the
particularities of real-life networks. Indeed, there has been significant
progress in understanding and modeling complex real-life networks in recent
years. However, the obtained results have had a minor influence on the
development of graph embedding algorithms. This paper aims to remedy this by
designing a graph embedding method that takes advantage of recent valuable
insights from the field of network science. More precisely, we present a novel
graph embedding approach based on the popularity-similarity and local
attraction paradigms. We evaluate the performance of the proposed approach on
the link prediction task on a large number of real-life networks. We show,
using extensive experimental analysis, that the proposed method outperforms
state-of-the-art graph embedding algorithms. We also demonstrate its robustness
to data scarcity and the choice of embedding dimensionality.
