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