Change Point Detection in Dynamic Graphs with Decoder-only Latent Space Model
Fuente:
arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866915246606647296 |
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| author | Kei, Yik Lun Li, Jialiang Li, Hangjian Chen, Yanzhen Padilla, Oscar Hernan Madrid |
| author_facet | Kei, Yik Lun Li, Jialiang Li, Hangjian Chen, Yanzhen Padilla, Oscar Hernan Madrid |
| contents | This manuscript studies the unsupervised change point detection problem in time series of graphs using a decoder-only latent space model. The proposed framework consists of learnable prior distributions for low-dimensional graph representations and of a decoder that bridges the observed graphs and latent representations. The prior distributions of the latent spaces are learned from the observed data as empirical Bayes to assist change point detection. Specifically, the model parameters are estimated via maximum approximate likelihood, with a Group Fused Lasso regularization imposed on the prior parameters. The augmented Lagrangian is solved via Alternating Direction Method of Multipliers, and Langevin Dynamics are recruited for posterior inference. Simulation studies show good performance of the latent space model in supporting change point detection and real data experiments yield change points that align with significant events. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_04719 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Change Point Detection in Dynamic Graphs with Decoder-only Latent Space Model Kei, Yik Lun Li, Jialiang Li, Hangjian Chen, Yanzhen Padilla, Oscar Hernan Madrid Methodology This manuscript studies the unsupervised change point detection problem in time series of graphs using a decoder-only latent space model. The proposed framework consists of learnable prior distributions for low-dimensional graph representations and of a decoder that bridges the observed graphs and latent representations. The prior distributions of the latent spaces are learned from the observed data as empirical Bayes to assist change point detection. Specifically, the model parameters are estimated via maximum approximate likelihood, with a Group Fused Lasso regularization imposed on the prior parameters. The augmented Lagrangian is solved via Alternating Direction Method of Multipliers, and Langevin Dynamics are recruited for posterior inference. Simulation studies show good performance of the latent space model in supporting change point detection and real data experiments yield change points that align with significant events. |
| title | Change Point Detection in Dynamic Graphs with Decoder-only Latent Space Model |
| topic | Methodology |
| url | https://arxiv.org/abs/2404.04719 |