Contributors and References
============================

Contributors    
------------    

    * `Annie Gray <https://anniegray52.github.io/>`_, lead developer
    * `Ed Davis <https://eddavis.uk/>`_, developer
    * `Ian Gallagher <https://www.iangallagher.uk/>`_, developer
    * `Alexander Modell <https://amodell.me/>`_, developer
    * `Patrick Rubin-Delanchy <https://www.maths.ed.ac.uk/~prd/index.html>`_, contributor
    * `Nick Whiteley <https://sites.google.com/view/nickwhiteley/>`_, contributor
    * `Dan Lawson <https://people.maths.bris.ac.uk/~madjl/>`_, contributor

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>`_