Deep Hashing for Signed Social Network Embedding

TL;DR

This paper introduces a deep hashing method for signed social network embedding that considers both positive and negative links, improving link prediction performance on real-world networks.

Contribution

It proposes a novel deep hashing approach that incorporates negative links in signed social network embedding, which was overlooked in previous feature hashing methods.

Findings

Outperforms state-of-the-art baselines in link prediction tasks

Effective in real-world signed social networks

Enhances network embedding by considering negative links

Abstract

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.