Work

Configurable hyperdimensional graph representation

Year: 2025

Type: article

Abstract: Graph analysis has emerged as a crucial field, offering versatile solutions for real-world data representation, from social networks to biological systems. However, the intricate nature of graphs often necessitates a degree of processing, such as learning mappings to a vector space, to perform analysis tasks like node classification and link prediction. A promising approach to this is Hyperdimensional Computing (HDC), inspired by neuroscience and mathematics. HDC utilizes high-dimensional vectors to efficiently manipulate complex data structures and perform operations like superposition and association, enhancing knowledge graph representations with contextual and semantic information. Nevertheless, addressing limitations in existing HDC-based approaches to graph representation is essential. This paper thoroughly explores these methods and presents ConfiGR: Configurable Graph Representation, a novel framework that introduces an adjustable design, enhancing its versatility across various graph types and tasks, ultimately boosting performance in multiple graph-related tasks.

Source: Artificial Intelligence

DOI: 10.1016/j.artint.2025.104384

Authors: Ali Zakeri, Zhuowen Zou, Hanning Chen, Mohsen Imani

Institution: University of California, Irvine

Language: English

Topic: Advanced Graph Neural Networks

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $4,260