  Learning representations of nodes in a low dimensional space is a crucial
task with many interesting applications in network analysis, including link
prediction and node classification. Two popular approaches for this problem
include matrix factorization and random walk-based models. In this paper, we
aim to bring together the best of both worlds, towards learning latent node
representations. In particular, we propose a weighted matrix factorization
model which encodes random walk-based information about the nodes of the graph.
The main benefit of this formulation is that it allows to utilize kernel
functions on the computation of the embeddings. We perform an empirical
evaluation on real-world networks, showing that the proposed model outperforms
baseline node embedding algorithms in two downstream machine learning tasks.
