HLSAD: Hodge Laplacian-based Simplicial Anomaly Detection
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915313579196416 |
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| author | Frantzen, Florian Schaub, Michael T. |
| author_facet | Frantzen, Florian Schaub, Michael T. |
| contents | In this paper, we propose HLSAD, a novel method for detecting anomalies in time-evolving simplicial complexes. While traditional graph anomaly detection techniques have been extensively studied, they often fail to capture changes in higher-order interactions that are crucial for identifying complex structural anomalies. These higher-order interactions can arise either directly from the underlying data itself or through graph lifting techniques. Our approach leverages the spectral properties of Hodge Laplacians of simplicial complexes to effectively model multi-way interactions among data points. By incorporating higher-dimensional simplicial structures into our method, our method enhances both detection accuracy and computational efficiency. Through comprehensive experiments on both synthetic and real-world datasets, we demonstrate that our approach outperforms existing graph methods in detecting both events and change points. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2505_24534 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | HLSAD: Hodge Laplacian-based Simplicial Anomaly Detection Frantzen, Florian Schaub, Michael T. Machine Learning Social and Information Networks In this paper, we propose HLSAD, a novel method for detecting anomalies in time-evolving simplicial complexes. While traditional graph anomaly detection techniques have been extensively studied, they often fail to capture changes in higher-order interactions that are crucial for identifying complex structural anomalies. These higher-order interactions can arise either directly from the underlying data itself or through graph lifting techniques. Our approach leverages the spectral properties of Hodge Laplacians of simplicial complexes to effectively model multi-way interactions among data points. By incorporating higher-dimensional simplicial structures into our method, our method enhances both detection accuracy and computational efficiency. Through comprehensive experiments on both synthetic and real-world datasets, we demonstrate that our approach outperforms existing graph methods in detecting both events and change points. |
| title | HLSAD: Hodge Laplacian-based Simplicial Anomaly Detection |
| topic | Machine Learning Social and Information Networks |
| url | https://arxiv.org/abs/2505.24534 |