Hyperbolic Node Embedding for Signed Networks
This work addresses the challenge of representing signed networks more effectively for applications like social network analysis, though it is incremental as it adapts existing hyperbolic methods to signed networks.
The authors tackled the problem of embedding signed networks by proposing a hyperbolic space approach to capture hierarchical structures, achieving superior performance over six Euclidean baselines across three tasks on seven real-world datasets.
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.