Distributed Representations of Signed Networks
arXiv:1702.06819 · doi:10.1007/978-3-319-93037-4_13
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.
Published in PAKDD 2018
References in corpus (5)
- DeepWalk: Online Learning of Social Representations
- LINE: Large-scale Information Network Embedding
- Predicting Positive and Negative Links in Online Social Networks
- sense2vec - A Fast and Accurate Method for Word Sense Disambiguation In Neural Word Embeddings
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Cited by in corpus (7)
- POLE: Polarized Embedding for Signed Networks
- Hyperbolic Node Embedding for Signed Networks
- CSNE: Conditional Signed Network Embedding
- SDGNN: Learning Node Representation for Signed Directed Networks
- Robust Deep Signed Graph Clustering via Weak Balance Theory
- Signed Graph Attention Networks
- Who will accept my request? Predicting response of link initiation in two-way relation networks