  Network embedding is a promising way of network representation, facilitating
many signed social network processing and analysis tasks such as link
prediction and node classification. Recently, feature hashing has been adopted
in several existing embedding algorithms to improve the efficiency, which has
obtained a great success. However, the existing feature hashing based embedding
algorithms only consider the positive links in signed social networks.
Intuitively, negative links can also help improve the performance. Thus, in
this paper, we propose a novel deep hashing method for signed social network
embedding by considering simultaneously positive and negative links. Extensive
experiments show that the proposed method performs better than several
state-of-the-art baselines through link prediction task over two real-world
signed social networks.
