Vertex misalignment and changepoint localization in network time series
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arXiv
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| Format: | Preprint |
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2026
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| author | Chen, Tianyi Kiasari, Mohammad Sharifi Yu, Sijing Park, Youngser Athreya, Avanti Lyzinski, Vince Priebe, Carey E Lubberts, Zachary |
| author_facet | Chen, Tianyi Kiasari, Mohammad Sharifi Yu, Sijing Park, Youngser Athreya, Avanti Lyzinski, Vince Priebe, Carey E Lubberts, Zachary |
| contents | Inference for time series of networks often relies on accurate vertex correspondence between network realizations at different times. In practice, however, such vertex alignments can be misspecified or unknown. We study the impact of vertex alignment on changepoint localization for dynamic networks through two illustrative models, each with a similar changepoint, with the key distinction being whether changepoint information is contained in marginal or joint distributions of the time-varying latent positions. We compare localization techniques ranging from the simple network statistic of average degree to the modern procedure of Euclidean mirrors. In one model, vertex misalignment causes little error, and in the other, it impairs localization in ways that cannot be corrected through graph matching or optimal transport, which we show are closely related in this setting. Our results demonstrate that robust network inference necessitates reckoning with the subtle interplay of marginal and joint information in the observed network time series. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_20072 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Vertex misalignment and changepoint localization in network time series Chen, Tianyi Kiasari, Mohammad Sharifi Yu, Sijing Park, Youngser Athreya, Avanti Lyzinski, Vince Priebe, Carey E Lubberts, Zachary Statistics Theory Methodology 62M99, 62G05, 62H12, 62F35 Inference for time series of networks often relies on accurate vertex correspondence between network realizations at different times. In practice, however, such vertex alignments can be misspecified or unknown. We study the impact of vertex alignment on changepoint localization for dynamic networks through two illustrative models, each with a similar changepoint, with the key distinction being whether changepoint information is contained in marginal or joint distributions of the time-varying latent positions. We compare localization techniques ranging from the simple network statistic of average degree to the modern procedure of Euclidean mirrors. In one model, vertex misalignment causes little error, and in the other, it impairs localization in ways that cannot be corrected through graph matching or optimal transport, which we show are closely related in this setting. Our results demonstrate that robust network inference necessitates reckoning with the subtle interplay of marginal and joint information in the observed network time series. |
| title | Vertex misalignment and changepoint localization in network time series |
| topic | Statistics Theory Methodology 62M99, 62G05, 62H12, 62F35 |
| url | https://arxiv.org/abs/2604.20072 |