Abstract:Many complex networks exhibit hierarchical, tree-like structures, making hyperbolic space a natural candidate wherein to learn representations of them. Based on this observation, Hyperbolic Graph Neural Networks (HGNNs) have been widely adopted as a principled choice for representation learning on tree-like graphs. In this work, we question this paradigm by proposing the additional condition of geometry--task alignment, i.e., whether the metric structure of the target follows that of the input graph. We theoretically and empirically demonstrate the capability of HGNNs to recover low-distortion representations on regression problems, and show that their geometric inductive bias becomes helpful when the problem requires preserving metric structure. By jointly analyzing predictive performance and embedding distortion, we further show that HGNNs gain an advantage on link prediction, a naturally geometry-aligned task, whereas this advantage largely disappears on standard node classification benchmarks, which are typically not geometry--aligned. Overall, our findings shift the focus from only asking "Is the graph hyperbolic?" to also questioning "Is the task aligned with hyperbolic geometry?", showing that HGNNs consistently outperform Euclidean models under such alignment, while their advantage vanishes otherwise.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2602.01828 [cs.LG] |
| (or arXiv:2602.01828v2 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2602.01828 arXiv-issued DOI via DataCite |
Submission history
From: Geri Skenderi [view email]
[v1]
Mon, 2 Feb 2026 09:01:58 UTC (815 KB)
[v2]
Thu, 14 May 2026 17:33:51 UTC (789 KB)