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

arXiv 2410.04251 ↗First posted 5 Oct 2024cs.LG cs.AI cs.CLcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 21 of its 58 references that are in the map. First posted on arXiv 5 Oct 2024.

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References · 21

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9 more
  1. 2023Label-free Node Classification on Graphs with Large Language Models (LLMS)cited by 72
  2. 2023Evaluating Graph Neural Networks for Link Prediction: Current Pitfalls and New Benchmarkingcited by 69
  3. 2023Neural Common Neighbor with Completion for Link Predictioncited by 64
  4. 2021On Positional and Structural Node Features for Graph Neural Networks on Non-attributed Graphscited by 49
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  6. 2021Variational Graph Normalized Auto-Encoderscited by 29
  7. 2019Predicting Research Trends with Semantic and Neural Networks with an application in Quantum Physicscited by 25
  8. 2019On Node Features for Graph Neural Networkscited by 21
  9. 2017Leveraging Node Attributes for Incomplete Relational Datacited by 10

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