Contributors and References

Contributors

References

If this package has been useful, please cite the relevant paper(s):

  • Whiteley, N., Gray, A. and Rubin-Delanchy, P., 2024. Statistical exploration of the Manifold Hypothesis. https://arxiv.org/abs/2208.11665

  • Gray, A., Modell, A., Rubin-Delanchy, P. and Whiteley, N., 2023. Hierarchical clustering with dot products recovers hidden tree structure. Advances in Neural Information Processing Systems (NeurIPS), 36. https://arxiv.org/abs/2305.15022

  • Modell, A., Gallagher, I., Ceccherini, E., Whiteley, N., and Rubin-Delanchy, P., 2023. Intensity Profile Projection: A framework for continuous-time representation learning for dynamic networks. Advances in Neural Information Processing Systems (NeurIPS), 36. https://arxiv.org/abs/2306.06155

  • Davis, E., Gallagher, I., Lawson, D.J. and Rubin-Delanchy, P., 2023. A simple and powerful framework for stable dynamic network embedding. https://arxiv.org/abs/2311.09251

  • Gallagher, I., Jones, A. and Rubin-Delanchy, P., 2021. Spectral embedding for dynamic networks with stability guarantees. Advances in Neural Information Processing Systems (NeurIPS), 34 https://arxiv.org/abs/2106.01282

  • Rubin-Delanchy, P., Cape, J., Tang, M., and Priebe, C. E. (2022). A statistical interpretation of spectral embedding: the generalised random dot product graph. Journal of the Royal Statistical Society Series B: Statistical Methodology, 84(4), 1446-1473. https://rss.onlinelibrary.wiley.com/doi/10.1111/rssb.12509

  • Whiteley, N., Gray, A., and Rubin-Delanchy, P. (2021). Matrix factorisation and the interpretation of geodesic distance. Advances in Neural Information Processing Systems (NeurIPS), 34, 24-38. https://arxiv.org/abs/2106.01260

  • Gallagher, I., Jones, A., Bertiger, A., Priebe, C. E., and Rubin-Delanchy, P. (2024). Spectral embedding of weighted graphs. Journal of the American Statistical Association (JASA), 119(547), 1923-1932. https://arxiv.org/abs/1910.05534

  • Modell, A., Gallagher, I., Cape, J. and Rubin-Delanchy, P., 2022. Spectral embedding and the latent geometry of multipartite networks. https://arxiv.org/abs/2202.03945

  • Jones, A. and Rubin-Delanchy, P., 2020. The multilayer random dot product graph. https://arxiv.org/abs/2007.10455

  • Levin, K., Athreya, A., Tang, M., Lyzinski, V. and Priebe, C.E., 2017. A central limit theorem for an omnibus embedding of multiple random dot product graphs. In 2017 IEEE international conference on data mining workshops. https://arxiv.org/abs/1705.09355