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

Gilchan Park, Paul Baity, Byung-Jun Yoon, Adolfy Hoisie

TL;DR

This study proposes initializing node features using LLMs to enhance node representations for link prediction tasks in graph neural networks, and demonstrates efficacy compared to traditional node embedding techniques.

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.

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

TL;DR

This study proposes initializing node features using LLMs to enhance node representations for link prediction tasks in graph neural networks, and demonstrates efficacy compared to traditional node embedding techniques.

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.
Paper Structure (14 sections, 2 figures, 4 tables, 1 algorithm)

This paper contains 14 sections, 2 figures, 4 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Creation of Semantic Network for Quantum Computing
  3. LLM-powered Concept Feature Embedding
  4. Experiments
  5. Experiment Setup
  6. Node Embedding Comparison Results
  7. Merging LLM embeddings
  8. Time Decayed Embedding
  9. Implications of Model Predictions: An Analytical Review by Domain Scientists
  10. Related Work
  11. Conclusion and Future Work
  12. Quantum Computing related Papers in arXiv
  13. Evaluation on Isolated Nodes
  14. Time Decay Embedding Generation

Figures (2)

  • Figure 1: The overview of future link predictions in the quantum computing semantic network using LLM-generated initial node features. In the example graph, solid lines indicate past established connections, while dotted lines represent a subset of potential future connections to be predicted by the model for relevance.
  • Figure 2: The number of quantum computing related papers in arXiv from 2007 to 2024 (as of June 15, 2024)