Bibliography

Sources cited and consulted in the TDA research programme.

62 items

  1. Esping-Andersen, G. (1999)

    Social foundations of postindustrial economies

    Oxford University Press

    DOI: 10.1093/0198742002.001.0001

  2. Esping-Andersen, G. (1990)

    The three worlds of welfare capitalism

    Princeton University Press

  3. Carrière et al. (2020)

    PersLay: A neural network layer for persistence diagrams and new graph topological signatures

    Link ↗

  4. Bodnar et al. (2021)

    Weisfeiler and Lehman go cellular: CW Networks

  5. Veličković et al. (2018)

    Graph attention networks

    arXiv preprint arXiv:1710.10903

    Link ↗

  6. Kipf & Welling (2017)

    Semi-supervised classification with graph convolutional networks

    Link ↗

  7. Hajij et al. (2022)

    Combinatorial complex neural networks

    arXiv preprint arXiv:2206.00606

    Link ↗

  8. Bronstein et al. (2021)

    Geometric deep learning: Grids, groups, graphs, geodesics, and gauges

    Link ↗

  9. McInnes et al. (2018)

    UMAP: Uniform manifold approximation and projection for dimension reduction

    arXiv preprint arXiv:1802.03426

    Link ↗

  10. HM Government, (2022)

    Levelling up the United Kingdom

  11. Tong, H. (1990)

    Non-linear time series: a dynamical system approach

    Oxford University Press

  12. Singer & Spilerman (1976)

    The representation of social processes by Markov models

    American Journal of Sociology

    DOI: 10.1086/226269

  13. Barrett & Carter (2013)

    The economics of poverty traps and persistent poverty: Empirical and policy implications

    Journal of Development Studies

    DOI: 10.1080/00220388.2013.785527

  14. Azariadis & Stachurski (2005)

    Poverty traps

    Elsevier

    DOI: 10.1016/S1574-0684(05)01005-1

  15. Vandecasteele, L. (2010)

    Poverty trajectories, triggering events and exit routes

    Sociological Research Online

    DOI: 10.5153/sro.2178

  16. Jenkins, S. (2011)

    Changing fortunes: Income mobility and poverty dynamics in Britain

    Oxford University Press

    DOI: 10.1093/acprof:oso/9780199226436.001.0001

  17. DiPrete & Eirich (2006)

    Cumulative advantage as a mechanism for inequality: A review of theoretical and empirical developments

    Annual Review of Sociology

    DOI: 10.1146/annurev.soc.32.061604.123127

  18. Savage et al. (2013)

    A new model of social class? Findings from the BBC's Great British Class Survey experiment

    Sociology-the Journal of The British Sociological Association

    DOI: 10.1177/0038038513481128

  19. Goldthorpe & Mills (2008)

    Trends in intergenerational class mobility in modern Britain: Evidence from national surveys, 1972–2005

    National Institute Economic Review

    DOI: 10.1177/0027950108096591

  20. Goldthorpe, J. (2016)

    Social class mobility in modern Britain: Changing structure, constant process

    Journal of the British Academy

    DOI: 10.5871/jba/004.089

  21. Bukodi & Goldthorpe (2019)

    Social mobility and education in Britain

    Cambridge University Press

    DOI: 10.1017/9781108567404

  22. Blanden et al. (2013)

    Intergenerational persistence in income and social class: The effect of within-group inequality

    Journal of the Royal Statistical Society: Series A

    DOI: 10.1111/j.1467-985X.2012.01467.x

  23. Blanden et al. (2004)

    Changes in intergenerational mobility in Britain

    Cambridge University Press

    DOI: 10.1017/CBO9780511492549.006

  24. Iacopini et al. (2019)

    Simplicial models of social contagion

    Nature Communications

    DOI: 10.1038/s41467-019-10431-6

  25. Sizemore et al. (2018)

    The importance of the whole: Topological data analysis for the network neuroscientist

    Network Neuroscience

    DOI: 10.1162/netn_a_00073

  26. Hiraoka et al. (2016)

    Hierarchical structures of amorphous solids characterized by persistent homology

    Proceedings of the National Academy of Sciences

    DOI: 10.1073/pnas.1520877113

  27. Rizvi et al. (2017)

    Single-cell topological RNA-seq analysis reveals insights into cellular differentiation and development

    Nature Biotechnology

    DOI: 10.1038/nbt.3854

  28. Saggar et al. (2018)

    Towards a new approach to reveal dynamical organization of the brain using topological data analysis

    Nature Communications

    DOI: 10.1038/s41467-018-03664-4

  29. Gidea & Katz (2018)

    Topological data analysis of financial time series: Landscapes of crashes

    Physica A: Statistical Mechanics and its Applications

    DOI: 10.1016/j.physa.2017.09.028

  30. Lapousière, D. (2024)

    Multipers: Efficient approximation of multiparameter persistence modules

    Journal of Open Source Software

    DOI: 10.21105/joss.06773

  31. Kerber & Schreiber (2019)

    Barcodes of towers and a streaming algorithm for persistent homology

    Discrete & Computational Geometry

    DOI: 10.1007/s00454-018-0030-0

  32. Carlsson & de Silva (2010)

    Zigzag persistence

    Foundations of Computational Mathematics

    DOI: 10.1007/s10208-010-9066-0

  33. Singh et al. (2007)

    Topological methods for the analysis of high dimensional data sets and 3D object recognition

    DOI: 10.2312/SPBG/SPBG07/091-100

  34. Stolz et al. (2017)

    Persistent homology of time-dependent functional networks constructed from coupled time series

    Chaos (Woodbury, N.Y.)

    DOI: 10.1063/1.4978997

  35. Bobrowski & Kahle (2018)

    Topology of random geometric complexes: A survey

    Journal of Applied and Computational Topology

    DOI: 10.1007/s41468-017-0010-0

  36. Atienza et al. (2019)

    Persistent entropy for separating topological features from noise in Vietoris–Rips complexes

    Journal of Intelligent Information Systems

    DOI: 10.1007/s10844-017-0473-4

  37. Turner et al. (2014)

    Fréchet means for distributions of persistence diagrams

    Discrete & Computational Geometry

    DOI: 10.1007/s00454-014-9604-7

  38. Robinson & Turner (2017)

    Hypothesis testing for topological data analysis

    Journal of Applied and Computational Topology

    DOI: 10.1007/s41468-017-0008-7

  39. de Silva & Carlsson (2004)

    Topological estimation using witness complexes

  40. Bauer, U. (2021)

    Ripser: Efficient computation of Vietoris–Rips persistence barcodes

    Journal of Applied and Computational Topology

    DOI: 10.1007/s41468-021-00071-5

  41. Cohen-Steiner et al. (2007)

    Stability of persistence diagrams

    Discrete & Computational Geometry

    DOI: 10.1007/s00454-006-1276-5

  42. Zomorodian & Carlsson (2005)

    Computing persistent homology

    Discrete & Computational Geometry

    DOI: 10.1007/s00454-004-1146-y

  43. Edelsbrunner et al. (2002)

    Topological persistence and simplification

    Discrete & Computational Geometry

    DOI: 10.1007/s00454-002-2885-2

  44. Edelsbrunner & Harer (2010)

    Computational topology: An introduction

    American Mathematical Society

  45. Carlsson, G. (2009)

    Topology and data

    Bulletin of the American Mathematical Society

    DOI: 10.1090/S0273-0979-09-01249-X

  46. Coulter et al. (2016)

    Re-thinking residential mobility: Linking lives through time and space

    Progress in Human Geography

    DOI: 10.1177/0309132515575417

  47. Elzinga & Liefbroer (2007)

    De-standardization of family-life trajectories of young adults: A cross-national comparison using sequence analysis

    European Journal of Population

    DOI: 10.1007/s10680-007-9133-7

  48. Halpin & Chan (1998)

    Class careers as sequences: An optimal matching analysis of work-life histories

    European Sociological Review

    DOI: 10.1093/oxfordjournals.esr.a018231

  49. Robette & Thibault (2008)

    Comparing qualitative harmonic analysis and optimal matching: An exploratory study of occupational trajectories

    Population-E

    DOI: 10.3917/pope.804.0621

  50. Studer & Ritschard (2016)

    What matters in differences between life trajectories: A comparative review of sequence dissimilarity measures

    Journal of the Royal Statistical Society: Series A

    DOI: 10.1111/rssa.12125

  51. Gauthier et al. (2010)

    Multichannel sequence analysis applied to social science data

    Sociological Methodology

    DOI: 10.1111/j.1467-9531.2010.01227.x

  52. Lesnard, L. (2010)

    Setting cost in optimal matching to uncover contemporaneous socio-temporal patterns

    Sociological Methods & Research

    DOI: 10.1177/0049124110362526

  53. Abbott & Tsay (2000)

    Sequence analysis and optimal matching methods in sociology

    Sociological Methods & Research

    DOI: 10.1177/0049124100029001001

  54. Abbott, A. (1995)

    Sequence analysis: New methods for old ideas

    Annual Review of Sociology

    DOI: 10.1146/annurev.so.21.080195.000521

  55. Edelsbrunner & Harer (2009)

    Computational Topology

    American Mathematical Society

    DOI: 10.1090/mbk/069

  56. Cohen-Steiner et al. (2006)

    Stability of Persistence Diagrams

    Discrete & Computational Geometry

    DOI: 10.1007/s00454-006-1276-5

  57. Bauer, U. (2021)

    Ripser: efficient computation of Vietoris–Rips persistence barcodes

    Journal of Applied and Computational Topology

    DOI: 10.1007/s41468-021-00071-5

  58. Carlsson, G. (2009)

    Topology and data

    Bulletin of the American Mathematical Society

    DOI: 10.1090/S0273-0979-09-01249-X

  59. Edelsbrunner et al. (2002)

    Topological Persistence and Simplification

    Discrete & Computational Geometry

    DOI: 10.1007/s00454-002-2885-2

  60. Loiseaux et al. (2023)

    Stable Vectorization of Multiparameter Persistent Homology using Signed Barcodes as Measures

    Link ↗

  61. Turner et al. (2014)

    Fréchet Means for Distributions of Persistence Diagrams

    Discrete & Computational Geometry

    DOI: 10.1007/s00454-014-9604-7

  62. Zomorodian & Carlsson (2004)

    Computing Persistent Homology

    Discrete & Computational Geometry

    DOI: 10.1007/s00454-004-1146-y