Signed Network Embedding with Dynamic Metric Learning

Abstract

Network embedding is an important method to learn low-dimensional vector representations of nodes in networks, which has wide-ranging applications in network analysis such as link prediction. Most existing network embedding models focus on the unsigned networks with only positive links. However, networks should have both positive and negative links in practical applications such as the trust and distrust relationships in social networks. It is certain that there are different properties between positive links and negative links, which means the network embedding models designed for unsigned networks are not suitable for signed networks. In this paper, we propose SNE-DML, a signed network embedding model with dynamic metric learning. The model learns positive and negative distance metrics respectively in the training process. We conduct sign prediction experiments on three datasets and compare with seven baselines including three signed network embedding models and four state-of-the-art unsigned network embedding models. The experimental results show the effectiveness of our model.