Enhancing Future Link Prediction in Quantum Computing Semantic Networks through LLM-Initiated Node Features
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910636627197952 |
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| author | Park, Gilchan Baity, Paul Yoon, Byung-Jun Hoisie, Adolfy |
| author_facet | Park, Gilchan Baity, Paul Yoon, Byung-Jun Hoisie, Adolfy |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_04251 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Enhancing Future Link Prediction in Quantum Computing Semantic Networks through LLM-Initiated Node Features Park, Gilchan Baity, Paul Yoon, Byung-Jun Hoisie, Adolfy Machine Learning Artificial Intelligence Computation and Language Social and Information Networks Quantum Physics 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. |
| title | Enhancing Future Link Prediction in Quantum Computing Semantic Networks through LLM-Initiated Node Features |
| topic | Machine Learning Artificial Intelligence Computation and Language Social and Information Networks Quantum Physics |
| url | https://arxiv.org/abs/2410.04251 |