Unsupervised Attributed Dynamic Network Embedding with Stability Guarantees Article Swipe

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Emma Ceccherini , Ian Gallagher , Andrew R. Jones ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2503.02859

Stability for dynamic network embeddings ensures that nodes behaving the same at different times receive the same embedding, allowing comparison of nodes in the network across time. We present attributed unfolded adjacency spectral embedding (AUASE), a stable unsupervised representation learning framework for dynamic networks in which nodes are attributed with time-varying covariate information. To establish stability, we prove uniform convergence to an associated latent position model. We quantify the benefits of our dynamic embedding by comparing with state-of-the-art network representation learning methods on four real attributed networks. To the best of our knowledge, AUASE is the only attributed dynamic embedding that satisfies stability guarantees without the need for ground truth labels, which we demonstrate provides significant improvements for link prediction and node classification.

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2503.02859
PDF: https://arxiv.org/pdf/2503.02859
OA Status: green
OpenAlex ID: https://openalex.org/W4416525308

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DOI: https://doi.org/10.48550/arxiv.2503.02859

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Title: Unsupervised Attributed Dynamic Network Embedding with Stability Guarantees

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Type: preprint

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Language: en

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Publication year: 2025

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Publication date: 2025-03-04

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Authors: Emma Ceccherini, Ian Gallagher, Andrew R. Jones

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Landing page: https://arxiv.org/abs/2503.02859

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PDF URL: https://arxiv.org/pdf/2503.02859

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Cited by: 0

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