Enhancing Future Link Prediction in Quantum Computing Semantic Networks through LLM-Initiated Node Features

TL;DR

This paper introduces a method that uses large language models to initialize node features in semantic networks, significantly improving link prediction accuracy in quantum computing research.

Contribution

It presents a novel approach of leveraging LLMs for node feature initialization, enhancing neural network-based link prediction in scientific semantic networks.

Findings

LLM-initialized features outperform traditional embeddings in link prediction tasks.

The method reduces manual feature engineering and associated costs.

Enhanced link prediction accuracy demonstrated on quantum computing semantic networks.

Abstract

Quantum computing is rapidly evolving in both physics and computer science, offering the potential to solve complex problems and accelerate computational processes. The development of quantum chips necessitates understanding the correlations among diverse experimental conditions. Semantic networks built on scientific literature, representing meaningful relationships between concepts, have been used across various domains to identify knowledge gaps and novel concept combinations. Neural network-based approaches have shown promise in link prediction within these networks. This study proposes initializing node features using LLMs to enhance node representations for link prediction tasks in graph neural networks. LLMs can provide rich descriptions, reducing the need for manual feature creation and lowering costs. Our method, evaluated using various link prediction models on a quantum computing semantic network, demonstrated efficacy compared to traditional node embedding techniques.