Graph Generation Powered with LLMs for Boosting Multivariate Time-Series Representation Learning
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866909824106627072 |
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| author | Wang, Yucheng Wu, Min Jin, Ruibing Li, Xiaoli Xie, Lihua Chen, Zhenghua |
| author_facet | Wang, Yucheng Wu, Min Jin, Ruibing Li, Xiaoli Xie, Lihua Chen, Zhenghua |
| contents | Sourced from multiple sensors and organized chronologically, Multivariate Time-Series (MTS) data involves crucial spatial-temporal dependencies. To capture these dependencies, Graph Neural Networks (GNNs) have emerged as powerful tools. As explicit graphs are not inherent to MTS data, graph generation becomes a critical first step in adapting GNNs to this domain. However, existing approaches often rely solely on the data itself for MTS graph generation, leaving them vulnerable to biases from small training datasets. This limitation hampers their ability to construct effective graphs, undermining the accurate modeling of underlying dependencies in MTS data and reducing GNN performance in this field. To address this challenge, we propose a novel framework, K-Link, leveraging the extensive universal knowledge encoded in Large Language Models (LLMs) to reduce biases for powered MTS graph generation. To harness the knowledge within LLMs, such as physical principles, we design and extract a \textit{Knowledge-Link graph} that captures universal knowledge of sensors and their linkage. To empower MTS graph generation with the knowledge-link graph, we further introduce a graph alignment module that transfers universal knowledge from the knowledge-link graph to the graph generated from MTS data. This enhances the MTS graph quality, ensuring effective representation learning for MTS data. Extensive experiments demonstrate the efficacy of K-Link for superior performance on various MTS tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_03645 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Graph Generation Powered with LLMs for Boosting Multivariate Time-Series Representation Learning Wang, Yucheng Wu, Min Jin, Ruibing Li, Xiaoli Xie, Lihua Chen, Zhenghua Artificial Intelligence Sourced from multiple sensors and organized chronologically, Multivariate Time-Series (MTS) data involves crucial spatial-temporal dependencies. To capture these dependencies, Graph Neural Networks (GNNs) have emerged as powerful tools. As explicit graphs are not inherent to MTS data, graph generation becomes a critical first step in adapting GNNs to this domain. However, existing approaches often rely solely on the data itself for MTS graph generation, leaving them vulnerable to biases from small training datasets. This limitation hampers their ability to construct effective graphs, undermining the accurate modeling of underlying dependencies in MTS data and reducing GNN performance in this field. To address this challenge, we propose a novel framework, K-Link, leveraging the extensive universal knowledge encoded in Large Language Models (LLMs) to reduce biases for powered MTS graph generation. To harness the knowledge within LLMs, such as physical principles, we design and extract a \textit{Knowledge-Link graph} that captures universal knowledge of sensors and their linkage. To empower MTS graph generation with the knowledge-link graph, we further introduce a graph alignment module that transfers universal knowledge from the knowledge-link graph to the graph generated from MTS data. This enhances the MTS graph quality, ensuring effective representation learning for MTS data. Extensive experiments demonstrate the efficacy of K-Link for superior performance on various MTS tasks. |
| title | Graph Generation Powered with LLMs for Boosting Multivariate Time-Series Representation Learning |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2403.03645 |