  Network embedding has attracted an increasing attention over the past few
years. As an effective approach to solve graph mining problems, network
embedding aims to learn a low-dimensional feature vector representation for
each node of a given network. The vast majority of existing network embedding
algorithms, however, are only designed for unsigned networks, and the signed
networks containing both positive and negative links, have pretty distinct
properties from the unsigned counterpart. In this paper, we propose a deep
network embedding model to learn the low-dimensional node vector
representations with structural balance preservation for the signed networks.
The model employs a semi-supervised stacked auto-encoder to reconstruct the
adjacency connections of a given signed network. As the adjacency connections
are overwhelmingly positive in the real-world signed networks, we impose a
larger penalty to make the auto-encoder focus more on reconstructing the scarce
negative links than the abundant positive links. In addition, to preserve the
structural balance property of signed networks, we design the pairwise
constraints to make the positively connected nodes much closer than the
negatively connected nodes in the embedding space. Based on the network
representations learned by the proposed model, we conduct link sign prediction
and community detection in signed networks. Extensive experimental results in
real-world datasets demonstrate the superiority of the proposed model over the
state-of-the-art network embedding algorithms for graph representation learning
in signed networks.
