Work

A Geometry-Aware Algorithm to Learn Hierarchical Embeddings in Hyperbolic Space

Year: 2024

Type: preprint

Abstract: Hyperbolic embeddings are a class of representation learning methods that offer competitive performances when data can be abstracted as a tree-like graph. However, in practice, learning hyperbolic embeddings of hierarchical data is difficult due to the different geometry between hyperbolic space and the Euclidean space. To address such difficulties, we first categorize three kinds of illness that harm the performance of the embeddings. Then, we develop a geometry-aware algorithm using a dilation operation and a transitive closure regularization to tackle these illnesses. We empirically validate these techniques and present a theoretical analysis of the mechanism behind the dilation operation. Experiments on synthetic and real-world datasets reveal superior performances of our algorithm.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2407.16641

Authors: Wang, Zhangyu, Xu, Lantian, Kong, Zhifeng, Wang, Weilong, Peng, Xuyu, Zheng, Enyang

Institutions: Alibaba Group (China), Carnegie Mellon University, University of California San Diego, Purdue University West Lafayette, University of California, Santa Barbara

Language: English

Topic: Advanced Graph Neural Networks

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W3154897940

Cites:

Cited by:

Related to: 20