Community detection and anomaly prediction in dynamic networks
Fuente:
arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866917850192543744 |
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| author | Safdari, Hadiseh De Bacco, Caterina |
| author_facet | Safdari, Hadiseh De Bacco, Caterina |
| contents | Anomaly detection is an essential task in the analysis of dynamic networks, offering early warnings of abnormal behavior. We present a principled approach to detect anomalies in dynamic networks that integrates community structure as a foundational model for regular behavior. Our model identifies anomalies as irregular edges while capturing structural changes. Our approach leverages a Markovian framework for temporal transitions and latent variables for community and anomaly detection, inferring hidden parameters to detect unusual interactions. Evaluations on synthetic and real-world datasets show strong anomaly detection across various scenarios. In a case study on professional football player transfers, we detect patterns influenced by club wealth and country, as well as unexpected transactions both within and across community boundaries. This work provides a framework for adaptable anomaly detection, highlighting the value of integrating domain knowledge with data-driven techniques for improved interpretability and robustness in complex networks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_10468 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Community detection and anomaly prediction in dynamic networks Safdari, Hadiseh De Bacco, Caterina Social and Information Networks 68-XX J.4; E.1 Anomaly detection is an essential task in the analysis of dynamic networks, offering early warnings of abnormal behavior. We present a principled approach to detect anomalies in dynamic networks that integrates community structure as a foundational model for regular behavior. Our model identifies anomalies as irregular edges while capturing structural changes. Our approach leverages a Markovian framework for temporal transitions and latent variables for community and anomaly detection, inferring hidden parameters to detect unusual interactions. Evaluations on synthetic and real-world datasets show strong anomaly detection across various scenarios. In a case study on professional football player transfers, we detect patterns influenced by club wealth and country, as well as unexpected transactions both within and across community boundaries. This work provides a framework for adaptable anomaly detection, highlighting the value of integrating domain knowledge with data-driven techniques for improved interpretability and robustness in complex networks. |
| title | Community detection and anomaly prediction in dynamic networks |
| topic | Social and Information Networks 68-XX J.4; E.1 |
| url | https://arxiv.org/abs/2404.10468 |