Distributed Representations of Signed Networks
This addresses the challenge of embedding signed networks for tasks like edge prediction, offering a domain-specific advancement in network analysis.
The paper tackles the problem of generating distributed representations for signed networks, which include edge polarities, by proposing SIGNet, a method that incorporates social balance theory and a novel node sampling strategy, achieving superior performance over state-of-the-art methods on multiple real-world datasets.
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.