Title: SIGNet: Scalable Embeddings for Signed Networks,
Abstract: Recent successes in word embedding and document embedding have motivated
researchers to explore similar representations for networks and to use such
representations for tasks such as edge prediction, node label prediction, and
community detection. Such network embedding methods are largely focused on
finding distributed representations for unsigned networks and are unable to
discover embeddings that respect polarities inherent in edges. We propose
SIGNet, a fast scalable embedding method suitable for signed networks. Our
proposed objective function aims to carefully model the social structure
implicit in signed networks by reinforcing the principles of social balance
theory. Our method builds upon the traditional word2vec family of embedding
approaches and adds a new targeted node sampling strategy to maintain
structural balance in higher-order neighborhoods. We demonstrate the
superiority of SIGNet over state-of-the-art methods proposed for both signed
and unsigned networks on several real world datasets from different domains. In
particular, SIGNet offers an approach to generate a richer vocabulary of
features of signed networks to support representation and reasoning.