BASSI: Balance and status combined signed network embedding

Abstract

Signed social networks have both positive and negative links which convey rich information such as trust or distrust, like or dislike. However, existing network embedding methods mostly focus on unsigned networks and ignore the negative interactions between users. In this paper, we investigate the problem of learning representations for signed networks and present a novel deep network structure to incorporate both the balance and status theory in signed networks. With the proposed framework, we can simultaneously learn the node embedding encoding the status of a node and the edge embedding denoting the sign of an edge. Furthermore, the learnt node and edge embeddings can be directly applied to the sign prediction and node ranking tasks. Experiments on real-world social networks demonstrate that our model significantly outperforms the state-of-the-art baselines.