SSNE: Status Signed Network Embedding

This work studies the problem of signed network embedding, which aims to obtain low-dimensional vectors for nodes in signed networks. Existing works mostly focus on learning representations via characterizing the social structural balance theory in signed networks. However, structural balance theory could not well satisfy some of the fundamental phenomena in real-world signed networks such as the direction of links. As a result, in this paper we integrate another theory Status Theory into signed network embedding since status theory can better explain the social mechanisms of signed networks. To be specific, we characterize the status of nodes in the semantic vector space and well design different ranking objectives for positive and negative links respectively. Besides, we utilize graph attention to assemble the information of neighborhoods. We conduct extensive experiments on three real-world datasets and the results show that our model can achieve a significant improvement compared with baselines.