Hyperbolic Node Embedding for Signed Networks
arXiv:1910.13090 · doi:10.1016/j.neucom.2020.10.008
Abstract
Signed network embedding methods aim to learn vector representations of nodes in signed networks. However, existing algorithms only managed to embed networks into low-dimensional Euclidean spaces whereas many intrinsic features of signed networks are reported more suitable for non-Euclidean spaces. For instance, previous works did not consider the hierarchical structures of networks, which is widely witnessed in real-world networks. In this work, we answer an open question that whether the hyperbolic space is a better choice to accommodate signed networks and learn embeddings that can preserve the corresponding special characteristics. We also propose a non-Euclidean signed network embedding method based on structural balance theory and Riemannian optimization, which embeds signed networks into a Poincaré ball in a hyperbolic space. This space enables our approach to capture underlying hierarchy of nodes in signed networks because it can be seen as a continuous tree. We empirically compare our method against six Euclidean-based baselines in three tasks on seven real-world datasets, and the results show the effectiveness of our method.
arXiv comment32 pages
References in corpus (16)
- Efficient Estimation of Word Representations in Vector Space
- Statistical mechanics of complex networks
- Hierarchical structure and the prediction of missing links in networks
- Hierarchical Organization in Complex Networks
- Graph Embedding Techniques, Applications, and Performance: A Survey
- Hyperbolic Geometry of Complex Networks
- Variational Graph Auto-Encoders
- Social Structure of Facebook Networks
- Efficiently Summarising Event Sequences with Rich Interleaving Patterns
- Stochastic gradient descent on Riemannian manifolds
- Hierarchy measure for complex networks
- Representation Tradeoffs for Hyperbolic Embeddings
- Poincaré GloVe: Hyperbolic Word Embeddings
- Distributed Representations of Signed Networks
- Signed Graph Convolutional Network
- Sitting closer to friends than enemies, revisited