Deep Hashing for Signed Social Network Embedding
This work addresses the challenge of efficiently representing signed social networks for tasks like link prediction, though it is incremental as it builds on existing feature hashing methods by adding negative link consideration.
The authors tackled the problem of signed social network embedding by proposing a deep hashing method that incorporates both positive and negative links, which improved performance over state-of-the-art baselines in link prediction tasks on two real-world networks.
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.