mlsanityData to 7 Oct 2026

Valid Bootstraps for Network Embeddings with Applications to Network Visualisation

arXiv 2410.20895 ↗First posted 28 Oct 2024stat.CO stat.AP stat.MLcited by 0

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Abstract (arXiv)

Quantifying uncertainty in networks is an important step in modelling relationships and interactions between entities. We consider the challenge of bootstrapping an inhomogeneous random graph when only a single observation of the network is made and the underlying data generating function is unknown. We address this problem by considering embeddings of the observed and bootstrapped network that are statistically indistinguishable. We utilise an exchangeable network test that can empirically validate bootstrap samples generated by any method. Existing methods fail this test, so we propose a principled, distribution-free network bootstrap using k-nearest neighbour smoothing, that can pass this exchangeable network test in many synthetic and real-data scenarios. We demonstrate the utility of this work in combination with the popular data visualisation method t-SNE, where uncertainty estimates from bootstrapping are used to explain whether visible structures represent real statistically sound structures.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 19 of its 53 references that are in the map. First posted on arXiv 28 Oct 2024.

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References · 19

  1. 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,346
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  3. 2016node2vec: Scalable Feature Learning for Networkscited by 1,913
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  9. 2020The multilayer random dot product graphcited by 18
  10. 2013DELTACON: A Principled Massive-Graph Similarity Functioncited by 17
  11. 2023Illuminati: Towards Explaining Graph Neural Networks for Cybersecurity Analysiscited by 14
  12. 2021Spectral embedding for dynamic networks with stability guaranteescited by 13
7 more
  1. 2016Estimating Psychological Networks and their Accuracy: A Tutorial Papercited by 12
  2. 2019Bootstrapping Networks with Latent Space Structurecited by 11
  3. 2013Subsampling bootstrap of count features of networkscited by 8
  4. 2017Bootstrapping Exchangeable Random Graphscited by 6
  5. 2020Multiple Network Embedding for Anomaly Detection in Time Series of Graphscited by 6
  6. 2024Valid Conformal Prediction for Dynamic GNNscited by 6
  7. 2023A Simple and Powerful Framework for Stable Dynamic Network Embeddingcited by 5

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