×

What are higher-order networks? (English) Zbl 1519.05226

Summary: Network-based modeling of complex systems and data using the language of graphs has become an essential topic across a range of different disciplines. Arguably, this graph-based perspective derives its success from the relative simplicity of graphs: A graph consists of nothing more than a set of vertices and a set of edges, describing relationships between pairs of such vertices. This simple combinatorial structure makes graphs interpretable and flexible modeling tools. The simplicity of graphs as system models, however, has been scrutinized in the literature recently. Specifically, it has been argued from a variety of different angles that there is a need for higher-order networks, which go beyond the paradigm of modeling pairwise relationships, as encapsulated by graphs. In this survey article we take stock of these recent developments. Our goals are to clarify (i) what higher-order networks are, (ii) why these are interesting objects of study, and (iii) how they can be used in applications.

MSC:

05C82 Small world graphs, complex networks (graph-theoretic aspects)
34B45 Boundary value problems on graphs and networks for ordinary differential equations
68R10 Graph theory (including graph drawing) in computer science
55U10 Simplicial sets and complexes in algebraic topology
05C65 Hypergraphs

References:

[1] P. A. Abrams, Arguments in favor of higher order interactions, Amer. Naturalist, 121 (1983), pp. 887-891, https://doi.org/10.1086/284111.
[2] J. Acebrón, L. Bonilla, C. Pérez Vicente, F. Ritort, and R. Spigler, The Kuramoto model: A simple paradigm for synchronization phenomena, Rev. Modern Phys., 77 (2005), pp. 137-185, https://doi.org/10.1103/RevModPhys.77.137.
[3] H. Adams, T. Emerson, M. Kirby, R. Neville, C. Peterson, P. Shipman, S. Chepushtanova, E. Hanson, F. Motta, and L. Ziegelmeier, Persistence images: A stable vector representation of persistent homology, J. Mach. Learn. Res., 18 (2017), art. 8. · Zbl 1431.68105
[4] M. Aguiar, C. Bick, and A. Dias, Network dynamics with higher-order interactions: Coupled cell hypernetworks for identical cells and synchrony, Nonlinearity, 36 (2023), pp. 4641-4673, https://doi.org/10.1088/1361-6544/ace39f. · Zbl 1525.37038
[5] M. A. D. Aguiar and A. P. S. Dias, Synchronization and equitable partitions in weighted networks, Chaos, 28 (2018), art. 073105, https://doi.org/10.1063/1.4997385. · Zbl 1425.34069
[6] P. Alexandroff, Elementary Concepts of Topology, Courier Corporation, 2012.
[7] S. Allesina and J. M. Levine, A competitive network theory of species diversity, Proc. Natl. Acad. Sci. USA, 108 (2011), pp. 5638-5642, https://doi.org/10.1073/pnas.1014428108.
[8] U. Alvarez-Rodriguez, F. Battiston, G. F. de Arruda, Y. Moreno, M. Perc, and V. Latora, Evolutionary dynamics of higher-order interactions in social networks, Nature Human Behaviour, 5 (2021), pp. 586-595, https://doi.org/10.1038/s41562-020-01024-1.
[9] E. J. Amézquita, M. Y. Quigley, T. Ophelders, E. Munch, and D. H. Chitwood, The shape of things to come: Topological data analysis and biology, from molecules to organisms, Developmental Dynam., 249 (2020), pp. 816-833.
[10] M. C. Angelini, F. Caltagirone, F. Krzakala, and L. Zdeborová, Spectral detection on sparse hypergraphs, in 2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton), IEEE, 2015, pp. 66-73.
[11] G. Ariav, A. Polsky, and J. Schiller, Submillisecond precision of the input-output transformation function mediated by fast sodium dendritic spikes in basal dendrites of CA\textup1 pyramidal neurons, J. Neurosci., 23 (2003), pp. 7750-7758, https://doi.org/10.1523/JNEUROSCI.23-21-07750.2003.
[12] P. Ashwin, S. Coombes, and R. Nicks, Mathematical frameworks for oscillatory network dynamics in neuroscience, J. Math. Neurosci., 6 (2016), art. 2, https://doi.org/10.1186/s13408-015-0033-6. · Zbl 1356.92015
[13] P. Ashwin and A. Rodrigues, Hopf normal form with \(S_N\) symmetry and reduction to systems of nonlinearly coupled phase oscillators, Phys. D, 325 (2016), pp. 14-24, https://doi.org/10.1016/j.physd.2016.02.009. · Zbl 1364.34041
[14] P. Ashwin and J. W. Swift, The dynamics of n weakly coupled identical oscillators, J. Nonlinear Sci., 2 (1992), pp. 69-108, https://doi.org/10.1007/BF02429852. · Zbl 0872.58049
[15] A. Aukerman, M. Carrière, C. Chen, K. Gardner, R. Rabadán, and R. Vanguri, Persistent homology based characterization of the breast cancer immune microenvironment: A feasibility study, in 36th International Symposium on Computational Geometry (SoCG 2020), Schloss Dagstuhl-Leibniz-Zentrum für Informatik, 2020. · Zbl 1518.92073
[16] G. Ausiello and L. Laura, Directed hypergraphs: Introduction and fundamental algorithms: A survey, Theoret. Comput. Sci., 658 (2017), pp. 293-306, https://doi.org/10.1016/j.tcs.2016.03.016. · Zbl 1356.68159
[17] E. Babson, C. Hoffman, and M. Kahle, The fundamental group of random \(2\)-complexes, J. Amer. Math. Soc., 24 (2011), pp. 1-28. · Zbl 1270.20042
[18] K. A. Bacik, M. T. Schaub, M. Beguerisse-Díaz, Y. N. Billeh, and M. Barahona, Flow-based network analysis of the Caenorhabditis elegans connectome, PLoS Comput. Biol., 12 (2016), art. e1005055.
[19] D. Bal, R. Berkowitz, P. Devlin, and M. Schacht, Hamiltonian Berge cycles in random hypergraphs, Combin. Probab. Comput., 30 (2021), pp. 228-238. · Zbl 1466.05145
[20] A.-L. Barabási, Network Science, Cambridge University Press, 2016. · Zbl 1353.94001
[21] S. Barbarossa and S. Sardellitti, Topological signal processing: Making sense of data building on multiway relations, IEEE Signal Process. Mag., 37 (2020), pp. 174-183.
[22] S. Barbarossa and S. Sardellitti, Topological signal processing over simplicial complexes, IEEE Trans. Signal Process., (2020). · Zbl 07590943
[23] S. Barbarossa, S. Sardellitti, and E. Ceci, Learning from signals defined over simplicial complexes, in 2018 IEEE Data Science Workshop (DSW), IEEE, 2018, pp. 51-55.
[24] S. Barbarossa and M. Tsitsvero, An introduction to hypergraph signal processing, in 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, 2016, pp. 6425-6429.
[25] D. S. Bassett and O. Sporns, Network neuroscience, Nature Neurosci., 20 (2017), pp. 353-364, https://doi.org/10.1038/nn.4502.
[26] F. Battiston, G. Cencetti, I. Iacopini, V. Latora, M. Lucas, A. Patania, J.-G. Young, and G. Petri, Networks beyond pairwise interactions: Structure and dynamics, Phys. Rep., 874 (2020), pp. 1-92. · Zbl 1472.05143
[27] M. Belkin and P. Niyogi, Laplacian eigenmaps for dimensionality reduction and data representation, Neural Comput., 15 (2003), pp. 1373-1396. · Zbl 1085.68119
[28] P. Bendich, J. S. Marron, E. Miller, A. Pieloch, and S. Skwerer, Persistent homology analysis of brain artery trees, Ann. Appl. Statist., 10 (2016), pp. 198-218.
[29] A. R. Benson, R. Abebe, M. T. Schaub, A. Jadbabaie, and J. Kleinberg, Simplicial closure and higher-order link prediction, Proc. Natl. Acad. Sci. USA, 115 (2018), pp. E11221-E11230.
[30] A. R. Benson, D. F. Gleich, and D. J. Higham, Higher-Order Network Analysis Takes Off, Fueled by Classical Ideas and New Data, preprint, https://arxiv.org/abs/2103.05031, 2021.
[31] A. R. Benson, D. F. Gleich, and J. Leskovec, Tensor spectral clustering for partitioning higher-order network structures, in Proceedings of the 2015 SIAM International Conference on Data Mining, SIAM, 2015, pp. 118-126, https://doi.org/10.1137/1.9781611974010.14.
[32] C. Berge, Graphs and Hypergraphs, North-Holland, Amsterdam, London, 1973. · Zbl 0254.05101
[33] C. Bick, Heteroclinic switching between chimeras, Phys. Rev. E, (2018), art. 050201, https://doi.org/10.1103/PhysRevE.97.050201.
[34] C. Bick, Heteroclinic dynamics of localized frequency synchrony: Heteroclinic cycles for small populations, J. Nonlinear Sci., 29 (2019), pp. 2547-2570, https://doi.org/10.1007/s00332-019-09552-5. · Zbl 1431.34045
[35] C. Bick, P. Ashwin, and A. Rodrigues, Chaos in generically coupled phase oscillator networks with nonpairwise interactions, Chaos, 26 (2016), art. 094814, https://doi.org/10.1063/1.4958928. · Zbl 1382.34038
[36] C. Bick, T. Böhle, and C. Kuehn, Phase oscillator networks with nonlocal higher-order interactions: Twisted states, stability, and bifurcations, SIAM J. Appl. Dynam. Syst., to appear. · Zbl 1523.35273
[37] C. Bick, T. Böhle, and C. Kuehn, Higher-Order Interactions in Phase Oscillator Networks through Phase Reductions of Oscillators with Phase Dependent Amplitude, preprint, https://doi.org/10.48550/arXiv.2305.04277, 2023. · Zbl 1523.35273
[38] C. Bick and M. J. Field, Asynchronous networks and event driven dynamics, Nonlinearity, 30 (2017), pp. 558-594, https://doi.org/10.1088/1361-6544/aa4f62. · Zbl 1361.34011
[39] C. Bick, M. Goodfellow, C. R. Laing, and E. A. Martens, Understanding the dynamics of biological and neural oscillator networks through exact mean-field reductions: A review, J. Math. Neurosci., 10 (2020), art. 9, https://doi.org/10.1186/s13408-020-00086-9. · Zbl 1448.92011
[40] C. Bick and A. Lohse, Heteroclinic dynamics of localized frequency synchrony: Stability of heteroclinic cycles and networks, J. Nonlinear Sci., 29 (2019), pp. 2571-2600, https://doi.org/10.1007/s00332-019-09562-3. · Zbl 1431.34046
[41] J. C. W. Billings, M. Hu, G. Lerda, A. N. Medvedev, F. Mottes, A. Onicas, A. Santoro, and G. Petri, Simplex2Vec Embeddings for Community Detection in Simplicial Complexes, preprint, https://arxiv.org/abs/1906.09068, 2019.
[42] A. S. Blevins and D. S. Bassett, Reorderability of node-filtered order complexes, Phys. Rev. E, 101 (2020), art. 052311.
[43] A. S. Blevins and D. S. Bassett, Topology in biology, in Handbook of the Mathematics of the Arts and Sciences, Springer, Cham, 2021, pp. 2073-2095. · Zbl 1470.00002
[44] O. Bobrowski and M. Kahle, Topology of random geometric complexes: A survey, J. Appl. Comput. Topol., 1 (2018), pp. 331-364, https://doi.org/10.1007/s41468-017-0010-0. · Zbl 1402.60015
[45] O. Bobrowski, M. Kahle, and P. Skraba, Maximally persistent cycles in random geometric complexes, Ann. Appl. Probab., 27 (2017), pp. 2032-2060. · Zbl 1377.60024
[46] S. Boccaletti, V. Latora, Y. Moreno, M. Chavez, and D.-U. Hwang, Complex networks: Structure and dynamics, Phys. Rep., 424 (2006), pp. 175-308. · Zbl 1371.82002
[47] C. Bodnar, F. Frasca, Y. G. Wang, N. Otter, G. Montúfar, P. Liò, and M. Bronstein, Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks, preprint, https://arxiv.org/abs/2103.03212, 2021.
[48] Á. Bodó, G. Y. Katona, and P. L. Simon, SIS epidemic propagation on hypergraphs, Bull. Math. Biol., 78 (2016), pp. 713-735. · Zbl 1339.05270
[49] P. Bonacich, A. C. Holdren, and M. Johnston, Hyper-edges and multidimensional centrality, Social Networks, 26 (2004), pp. 189-203.
[50] S. Bressan, J. Li, S. Ren, and J. Wu, The Embedded Homology of Hypergraphs and Applications, preprint, https://arxiv.org/abs/1610.00890, 2016. · Zbl 1432.55032
[51] S. C. Brüningk, F. Hensel, C. R. Jutzeler, and B. Rieck, Image Analysis for Alzheimer’s Disease Prediction: Embracing Pathological Hallmarks for Model Architecture Design, preprint, https://arxiv.org/abs/2011.06531, 2020.
[52] P. Bubenik, Statistical topological data analysis using persistence landscapes., J. Mach. Learn. Res., 16 (2015), pp. 77-102. · Zbl 1337.68221
[53] E. Bunch, Q. You, G. Fung, and V. Singh, Simplicial \(2\)-Complex Convolutional Neural Nets, preprint, https://arxiv.org/abs/2012.06010, 2020.
[54] H. M. Byrne, H. A. Harrington, R. Muschel, G. Reinert, B. Stolz-Pretzer, and U. Tillmann, Topology characterises tumour vasculature, Math. Today (Southend-on-Sea), 55 (2019), pp. 206-210.
[55] D. Calugaru, J. F. Totz, E. A. Martens, and H. Engel, First-order synchronization transition in a large population of strongly coupled relaxation oscillators, Sci. Adv., 6 (2020), art. eabb2637, https://doi.org/10.1126/sciadv.abb2637.
[56] O. Candogan, I. Menache, A. Ozdaglar, and P. A. Parrilo, Flows and decompositions of games: Harmonic and potential games, Math. Oper. Res., 36 (2011), pp. 474-503. · Zbl 1239.91006
[57] T. Carletti, F. Battiston, G. Cencetti, and D. Fanelli, Random walks on hypergraphs, Phys. Rev. E, 101 (2020), art. 022308.
[58] T. Carletti, D. Fanelli, and S. Nicoletti, Dynamical systems on hypergraphs, J. Phys. Complexity, 1 (2020), art. 035006.
[59] G. Carlsson, Topology and data, Bull. Amer. Math. Soc., 46 (2009), pp. 255-308. · Zbl 1172.62002
[60] G. Carlsson and V. De Silva, Zigzag persistence, Found. Comput. Math., 10 (2010), pp. 367-405. · Zbl 1204.68242
[61] G. Carlsson, V. De Silva, and D. Morozov, Zigzag persistent homology and real-valued functions, in Proceedings of the Twenty-Fifth Annual Symposium on Computational Geometry, 2009, pp. 247-256. · Zbl 1380.68385
[62] G. Carlsson, A. Dwaraknath, and B. J. Nelson, Persistent and Zigzag Homology: A Matrix Factorization Viewpoint, preprint, https://arxiv.org/abs/1911.10693, 2019.
[63] G. Carlsson, G. Singh, and A. Zomorodian, Computing multidimensional persistence, in International Symposium on Algorithms and Computation, Springer, 2009, pp. 730-739. · Zbl 1273.68416
[64] G. Carlsson and A. Zomorodian, The theory of multidimensional persistence, Discrete Comput. Geom., 42 (2009), pp. 71-93. · Zbl 1187.55004
[65] J. M. Chan, G. Carlsson, and R. Rabadan, Topology of viral evolution, Proc. Natl. Acad. Sci. USA, 110 (2013), pp. 18566-18571. · Zbl 1292.92014
[66] T.-H. H. Chan and Z. Liang, Generalizing the hypergraph Laplacian via a diffusion process with mediators, Theoret. Comput. Sci., 806 (2020), pp. 416-428. · Zbl 1442.05145
[67] T.-H. H. Chan, A. Louis, Z. G. Tang, and C. Zhang, Spectral properties of hypergraph Laplacian and approximation algorithms, J. ACM, 65 (2018), art. 15. · Zbl 1426.05163
[68] T. Chaplin, First Betti Number of the Path Homology of Random Directed Graphs, preprint, https://arxiv.org/abs/2111.13493, 2021.
[69] F. Chazal, B. Fasy, F. Lecci, B. Michel, A. Rinaldo, and L. Wasserman, Subsampling methods for persistent homology, in International Conference on Machine Learning, PMLR, 2015, pp. 2143-2151.
[70] F. Chazal and B. Michel, An introduction to topological data analysis: Fundamental and practical aspects for data scientists, Frontiers Artificial Intell., 4 (2021).
[71] J.-R. Chazottes and B. Fernandez, Dynamics of Coupled Map Lattices and of Related Spatially Extended Systems, Lecture Notes in Phys. 671, Springer, Berlin, Heidelberg, 2005, https://doi.org/10.1007/b103930. · Zbl 1073.70002
[72] Y.-C. Chen, M. Meilă, and I. G. Kevrekidis, Helmholtzian Eigenmap: Topological Feature Discovery & Edge Flow Learning from Point Cloud Data, preprint, https://arxiv.org/abs/2103.07626, 2021.
[73] I. Chien, C.-Y. Lin, and I.-H. Wang, Community detection in hypergraphs: Optimal statistical limit and efficient algorithms, in International Conference on Artificial Intelligence and Statistics, 2018, pp. 871-879.
[74] P. Chodrow and A. Mellor, Annotated hypergraphs: Models and applications, Appl. Network Sci., 5 (2020), art. 9.
[75] P. S. Chodrow, Configuration Models of Random Hypergraphs, preprint, https://arxiv.org/abs/1902.09302, 2019. · Zbl 1467.05239
[76] P. S. Chodrow, N. Veldt, and A. R. Benson, Generative hypergraph clustering: From blockmodels to modularity, Sci. Adv., 7 (2021), art. eabh1303.
[77] S. Chowdhury, S. Huntsman, and M. Yutin, Path homology and temporal networks, in International Conference on Complex Networks and Their Applications, Springer, 2020, pp. 639-650.
[78] S. Chowdhury and F. Mémoli, Persistent path homology of directed networks, in Proceedings of the Twenty-Ninth Annual ACM-SIAM Symposium on Discrete Algorithms, SIAM, 2018, pp. 1152-1169, https://doi.org/10.1137/1.9781611975031.75. · Zbl 1403.68154
[79] F. R. Chung and F. C. Graham, Spectral Graph Theory, CBMS Reg. Conf. Ser. Math. 92, American Mathematical Society, 1997. · Zbl 0867.05046
[80] D. Clemens, J. Ehrenmüller, and Y. Person, A Dirac-type theorem for Berge cycles in random hypergraphs, Electron. J. Combin., 27 (2020), pp. 3-39. · Zbl 1466.05194
[81] D. Cohen-Steiner, H. Edelsbrunner, and J. Harer, Stability of persistence diagrams, Discrete Comput. Geom., 37 (2007), pp. 103-120. · Zbl 1117.54027
[82] R. R. Coifman and S. Lafon, Diffusion maps, Appl. Comput. Harmon. Anal., 21 (2006), pp. 5-30. · Zbl 1095.68094
[83] O. Cooley, N. D. Giudice, M. Kang, and P. Sprüssel, Vanishing of cohomology groups of random simplicial complexes, Random Structures Algorithms, 56 (2020), pp. 461-500. · Zbl 1436.05095
[84] A. Costa and M. Farber, Large random simplicial complexes, I, J. Topol. Anal., 8 (2016), pp. 399-429. · Zbl 1339.05440
[85] A. Costa and M. Farber, Random simplicial complexes, in Configuration Spaces, Springer, 2016, pp. 129-153. · Zbl 1398.55011
[86] A. Costa and M. Farber, Large random simplicial complexes, II: The fundamental group, J. Topol. Anal., 9 (2017), pp. 441-483. · Zbl 1378.55011
[87] A. Costa and M. Farber, Large random simplicial complexes, III: The critical dimension, J. Knot Theory Ramifications, 26 (2017), art. 1740010. · Zbl 1367.55011
[88] A. Costa, M. Farber, and T. Kappeler, Topics of stochastic algebraic topology, in Proceedings of the Workshop on Geometric and Topological Methods in Computer Science, Electron Notes Theoret. Comput. Sci. 283, Elsevier, 2012, pp. 53-70. · Zbl 1347.05225
[89] O. T. Courtney and G. Bianconi, Generalized network structures: The configuration model and the canonical ensemble of simplicial complexes, Phys. Rev. E, 93 (2016), art. 062311.
[90] L. Crawford, A. Monod, A. X. Chen, S. Mukherjee, and R. Rabadán, Topological Summaries of Tumor Images Improve Prediction of Disease Free Survival in Glioblastoma Multiforme, preprint, https://arxiv.org/abs/1611.06818, 2016.
[91] C. Curto, What can topology tell us about the neural code?, Bull. Amer. Math. Soc., 54 (2017), pp. 63-78. · Zbl 1353.92027
[92] G. F. de Arruda, G. Petri, and Y. Moreno, Social contagion models on hypergraphs, Phys. Rev. Res., 2 (2020), art. 023032.
[93] V. De Silva and R. Ghrist, Homological sensor networks, Notices Amer. Math. Soc., 54 (2007), pp. 10-17. · Zbl 1142.94006
[94] V. De Silva, D. Morozov, and M. Vejdemo-Johansson, Dualities in persistent (co)homology, Inverse Problems, 27 (2011), art. 124003. · Zbl 1247.68307
[95] L. DeVille, Consensus on Simplicial Complexes, or: The Nonlinear Simplicial Laplacian, preprint, https://arxiv.org/abs/2010.07421, 2020.
[96] T. K. Dey, T. Li, and Y. Wang, An Efficient Algorithm for \(1 \)-Dimensional (Persistent) Path Homology, preprint, https://arxiv.org/abs/2001.09549, 2020.
[97] A. Dudek and A. Frieze, Tight Hamilton cycles in random uniform hypergraphs, Random Structures Algorithms, 42 (2013), pp. 374-385. · Zbl 1264.05078
[98] D. Easley and J. Kleinberg, Networks, Crowds, and Markets, Cambridge University Press, Cambridge, UK, 2010. · Zbl 1205.91007
[99] S. Ebli, M. Defferrard, and G. Spreemann, Simplicial Neural Networks, preprint, https://arxiv.org/abs/2010.03633, 2020.
[100] B. Eckmann, Harmonische Funktionen und Randwertaufgaben in einem Komplex, Comment. Math. Helv., 17 (1944), pp. 240-255. · Zbl 0061.41106
[101] H. Edelsbrunner and J. Harer, Persistent homology: A survey, Contemp. Math., 453 (2008), pp. 257-282. · Zbl 1145.55007
[102] H. Edelsbrunner and J. Harer, Computational Topology: An Introduction, American Mathematical Society, 2010. · Zbl 1193.55001
[103] F. Effenberger and J. Spreer, simpcomp: A GAP toolbox for simplicial complexes, ACM Commun. Comput. Algebra, 44 (2011), pp. 186-189. · Zbl 1308.68167
[104] M. Eidi, A. Farzam, W. Leal, A. Samal, and J. Jost, Edge-based analysis of networks: Curvatures of graphs and hypergraphs, Theory Biosci., 139 (2020), pp. 337-348.
[105] E. Estrada and J. A. Rodríguez-Velázquez, Subgraph centrality and clustering in complex hyper-networks, Phys. A, 364 (2006), pp. 581-594.
[106] G. Facchetti, G. Iacono, and C. Altafini, Computing global structural balance in large-scale signed social networks, Proc. Natl. Acad. Sci. USA, 108 (2011), pp. 20953-20958.
[107] L. Fajstrup, E. Goubault, E. Haucourt, S. Mimram, and M. Raussen, Directed Algebraic Topology and Concurrency, Springer, 2016. · Zbl 1338.68003
[108] M. Farber and L. Mead, Random simplicial complexes in the medial regime, Topology Appl., 272 (2020), art. 107065. · Zbl 1435.57022
[109] M. Farber, L. Mead, and T. Nowik, Random Simplicial Complexes, Duality and the Critical Dimension, preprint, https://arxiv.org/abs/1901.09578, 2019. · Zbl 1493.57020
[110] B. T. Fasy, J. Kim, F. Lecci, and C. Maria, Introduction to the R package TDA, preprint, https://arxiv.org/abs/1411.1830, 2014.
[111] B. T. Fasy, F. Lecci, A. Rinaldo, L. Wasserman, S. Balakrishnan, and A. Singh, Confidence sets for persistence diagrams, Ann. Statist., 42 (2014), pp. 2301-2339. · Zbl 1310.62059
[112] N. Fenichel, Persistence and smoothness of invariant manifolds for flows, Indiana Univ. Math. J., 21 (1972), pp. 193-226. · Zbl 0246.58015
[113] E. Ferraz and A. Vergne, Statistics of Geometric Random Simplicial Complexes, 2011, https://hal.science/hal-00591670.
[114] S. Fortunato and D. Hric, Community detection in networks: A user guide, Phys. Rep., 659 (2016), pp. 1-44.
[115] B. K. Fosdick, D. B. Larremore, J. Nishimura, and J. Ugander, Configuring random graph models with fixed degree sequences, SIAM Rev., 60 (2018), pp. 315-355, https://doi.org/10.1137/16M1087175. · Zbl 1387.05235
[116] C. F. Fowler, Generalized Random Simplicial Complexes, preprint, https://arxiv.org/abs/1503.01831, 2015.
[117] C. F. Fowler, Homology of multi-parameter random simplicial complexes, Discrete Comput. Geom., 62 (2019), pp. 87-127. · Zbl 1414.05266
[118] N. E. Friedkin, Theoretical foundations for centrality measures, Amer. J. Soc., 96 (1991), pp. 1478-1504.
[119] L. Gauvin, A. Panisson, and C. Cattuto, Detecting the community structure and activity patterns of temporal networks: A non-negative tensor factorization approach, PloS One, 9 (2014), art. e86028.
[120] E. Gawrilow and M. Joswig, polymake: A framework for analyzing convex polytopes, in Polytopes-Combinatorics and Computation, Springer, 2000, pp. 43-73. · Zbl 0960.68182
[121] G. Ghoshal, V. Zlatić, G. Caldarelli, and M. E. Newman, Random hypergraphs and their applications, Phys. Rev. E, 79 (2009), art. 066118.
[122] D. Ghoshdastidar and A. Dukkipati, Consistency of spectral partitioning of uniform hypergraphs under planted partition model, in Advances in Neural Information Processing Systems, 2014, pp. 397-405. · Zbl 1360.62330
[123] D. Ghoshdastidar and A. Dukkipati, Consistency of spectral hypergraph partitioning under planted partition model, Ann. Statist., 45 (2017), pp. 289-315. · Zbl 1360.62330
[124] R. Ghrist, Barcodes: The persistent topology of data, Bull. Amer. Math. Soc., 45 (2008), pp. 61-75. · Zbl 1391.55005
[125] R. W. Ghrist, Elementary Applied Topology, Vol. 1, Createspace Seattle, 2014. · Zbl 1427.55001
[126] P. Giblin, Graphs, Surfaces and Homology: An Introduction to Algebraic Topology, Springer Science & Business Media, 2013.
[127] C. Giusti, E. Pastalkova, C. Curto, and V. Itskov, Clique topology reveals intrinsic geometric structure in neural correlations, Proc. Natl. Acad. Sci. USA, 112 (2015), pp. 13455-13460. · Zbl 1355.92015
[128] N. Glaze, T. M. Roddenberry, and S. Segarra, Principled Simplicial Neural Networks for Trajectory Prediction, preprint, https://arxiv.org/abs/2102.10058, 2021.
[129] D. F. Gleich, PageRank beyond the web, SIAM Rev., 57 (2015), pp. 321-363, https://doi.org/10.1137/140976649. · Zbl 1336.05122
[130] D. F. Gleich, L.-H. Lim, and Y. Yu, Multilinear PageRank, SIAM J. Matrix Anal. Appl., 36 (2015), pp. 1507-1541, https://doi.org/10.1137/140985160. · Zbl 1330.15029
[131] G. Gonzalez, A. Ushakova, R. Sazdanovic, and J. Arsuaga, Prediction in cancer genomics using topological signatures and machine learning, in Topological Data Analysis, Springer, 2020, pp. 247-276. · Zbl 1448.62165
[132] L. J. Grady and J. R. Polimeni, Discrete Calculus: Applied Analysis on Graphs for Computational Science, Springer Science & Business Media, 2010. · Zbl 1195.68074
[133] D. R. Grayson and M. E. Stillman, Macaulay2, a Software System for Research in Algebraic Geometry; available from http://www.math.uiuc.edu/Macaulay2/.
[134] A. Grigor’yan, Y. Lin, Y. V. Muranov, and S.-T. Yau, Path complexes and their homologies, J. Math. Sci., 248 (2020), pp. 564-599. · Zbl 1448.05171
[135] J. Grilli, G. Barabás, M. J. Michalska-Smith, and S. Allesina, Higher-order interactions stabilize dynamics in competitive network models, Nature, 548 (2017), pp. 210-213.
[136] E. Gross, V. Karwa, and S. Petrović, Algebraic Statistics, Tables, and Networks: The Fienberg Advantage, preprint, https://arxiv.org/abs/1910.01692, 2019.
[137] E. Gross, S. Petrović, and D. Stasi, Goodness-of-fit for log-linear network models: Dynamic Markov bases using hypergraphs, Ann. Inst. Statist. Math., 69 (2017), pp. 673-704, https://doi.org/10.1007/s10463-016-0560-2. · Zbl 1400.62124
[138] E. Gross, S. Petrović, and D. Stasi, Random Graphs with Node and Block Effects: Models, Goodness-of-Fit Tests, and Applications to Biological Networks, preprint, https://arxiv.org/abs/2104.03167, 2021.
[139] C. Hacker, k-simplex2vec: A Simplicial Extension of node2vec, preprint, https://arxiv.org/abs/2010.05636, 2020.
[140] V. Hakim and W.-J. Rappel, Dynamics of the globally coupled complex Ginzburg-Landau equation, Phys. Rev. A, 46 (1992), pp. R7347-R7350, https://doi.org/10.1103/PhysRevA.46.R7347.
[141] J. Harer, K. Mischaikow, and S. Mukherjee, Inferring Network Controls from Topology Using the Chomp Database, Tech. report, Duke University, Durham, NC, 2015.
[142] H. A. Harrington, N. Otter, H. Schenck, and U. Tillmann, Stratifying multiparameter persistent homology, SIAM J. Appl. Algebra Geom., 3 (2019), pp. 439-471, https://doi.org/10.1137/18M1224350. · Zbl 1450.55002
[143] R. Harris-Warrick, E. Marder, A. I. Selverston, and M. Moulins, eds., Dynamic Biological Networks: The Stomatogastric Nervous System, MIT Press, 1992, https://mitpress.mit.edu/books/dynamic-biological-networks.
[144] A. Hatcher, Algebraic Topology, Cambridge University Press, 2002, http://www.math.cornell.edu/ hatcher/AT/ATpage.html. · Zbl 1044.55001
[145] D. Heger and K. Krischer, Robust autoassociative memory with coupled networks of Kuramoto-type oscillators, Phys. Rev. E, 94 (2016), art. 022309, https://doi.org/10.1103/PhysRevE.94.022309.
[146] M. Hein, S. Setzer, L. Jost, and S. S. Rangapuram, The total variation on hypergraphs-learning on hypergraphs revisited, Adv. Neural Inform. Process. Syst., 26 (2013).
[147] A. Helm, A. S. Blevins, and D. S. Bassett, The Growing Topology of the C. Elegans Connectome, preprint, https://arxiv.org/abs/2101.00065, 2020.
[148] G. Henselman and R. Ghrist, Matroid Filtrations and Computational Persistent Homology, preprint, https://arxiv.org/abs/1606.00199, 2016.
[149] K. Hess, Topological adventures in neuroscience, in Topological Data Analysis, Springer, 2020, pp. 277-305. · Zbl 1451.92071
[150] A. Hickok, Y. Kureh, H. Z. Brooks, M. Feng, and M. A. Porter, A Bounded-Confidence Model of Opinion Dynamics on Hypergraphs, preprint, https://arxiv.org/abs/2102.06825, 2021. · Zbl 1482.91163
[151] D. J. Higham and H.-L. de Kergorlay, Mean field analysis of hypergraph contagion models, SIAM J. Appl. Math., 82 (2022), pp. 1987-2007, https://doi.org/10.1137/21M1440219. · Zbl 1503.92064
[152] Y. Hiraoka, T. Nakamura, A. Hirata, E. G. Escolar, K. Matsue, and Y. Nishiura, Hierarchical structures of amorphous solids characterized by persistent homology, Proc. Natl. Acad. Sci. USA, 113 (2016), pp. 7035-7040.
[153] C. Hoffman, M. Kahle, and E. Paquette, Spectral gaps of random graphs and applications, Internat. Math. Res. Not. IMRN, 11 (2021), pp. 8353-8404, https://doi.org/10.1093/imrn/rnz077. · Zbl 1473.05285
[154] D. Horak and J. Jost, Spectra of combinatorial Laplace operators on simplicial complexes, Adv. Math., 244 (2013), pp. 303-336. · Zbl 1290.05103
[155] L. Horstmeyer and C. Kuehn, Adaptive voter model on simplicial complexes, Phys. Rev. E, 101 (2020), art. 022305, https://doi.org/10.1103/PhysRevE.101.022305.
[156] D. R. Hunter, M. S. Handcock, C. T. Butts, S. M. Goodreau, and M. Morris, ergm: A package to fit, simulate and diagnose exponential-family models for networks, J. Statist. Software, 24 (2008), art. nihpa54860.
[157] I. Iacopini, G. Petri, A. Barrat, and V. Latora, Simplicial models of social contagion, Nature Commun., 10 (2019), art. 2485, https://doi.org/10.1038/s41467-019-10431-6.
[158] S. Jahnke, M. Timme, and R.-M. Memmesheimer, A unified dynamic model for learning, replay, and sharp-wave/ripples, J. Neurosci., 35 (2015), pp. 16236-16258, https://doi.org/10.1523/JNEUROSCI.3977-14.2015.
[159] J. Jia, M. T. Schaub, S. Segarra, and A. R. Benson, Graph-based semi-supervised & active learning for edge flows, in Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019, pp. 761-771.
[160] X. Jiang, L.-H. Lim, Y. Yao, and Y. Ye, Statistical ranking and combinatorial Hodge theory, Math. Program., 127 (2011), pp. 203-244. · Zbl 1210.90142
[161] T. Kaczynski, K. Mischaikow, and M. Mrozek, Computational Homology, Appl. Math. Sci. 157, Springer Science & Business Media, 2006.
[162] M. Kahle, Topology of random clique complexes, Discrete Math., 309 (2009), pp. 1658-1671, https://doi.org/10.1016/j.disc.2008.02.037. · Zbl 1215.05163
[163] M. Kahle, Random geometric complexes, Discrete Comput. Geom., 45 (2011), pp. 553-573. · Zbl 1219.05175
[164] M. Kahle, Sharp vanishing thresholds for cohomology of random flag complexes, Ann. of Math. (2), 179 (2014), pp. 1085-1107. · Zbl 1294.05195
[165] M. Kahle, Topology of random simplicial complexes: A survey, in Algebraic Topology: Applications and New Directions, Contemp. Math. 620, American Mathematical Society, 2014, pp. 201-222. · Zbl 1294.00030
[166] M. Kahle and E. Meckes, Limit the theorems for Betti numbers of random simplicial complexes, Homology Homotopy Appl., 15 (2013), pp. 343-374. · Zbl 1268.05180
[167] L. Kanari, P. Dłotko, M. Scolamiero, R. Levi, J. Shillcock, K. Hess, and H. Markram, A topological representation of branching neuronal morphologies, Neuroinform., 16 (2018), pp. 3-13.
[168] M. Karoński and T. Łuczak, The phase transition in a random hypergraph, J. Comput. Appl. Math., 142 (2002), pp. 125-135. · Zbl 0995.05131
[169] V. Karwa, M. J. Pelsmajer, S. Petrović, D. Stasi, and D. Wilburne, Statistical models for cores decomposition of an undirected random graph, Electron. J. Statist., 11 (2017), pp. 1949-1982. · Zbl 1386.05178
[170] V. Karwa and S. Petrović, Discussion of “coauthorship and citation networks for statisticians,” Ann. Appl. Statist., 10 (2016), pp. 1827-1834. · Zbl 1454.62543
[171] Z. T. Ke, F. Shi, and D. Xia, Community Detection for Hypergraph Networks via Regularized Tensor Power Iteration, preprint, https://arxiv.org/abs/1909.06503, 2019.
[172] M. Kerber and A. Rolle, Fast minimal presentations of bi-graded persistence modules, in 2021 Proceedings of the Workshop on Algorithm Engineering and Experiments (ALENEX), SIAM, 2021, pp. 207-220, https://doi.org/10.1137/1.9781611976472.16. · Zbl 07302448
[173] C. Kim, A. S. Bandeira, and M. X. Goemans, Community detection in hypergraphs, spiked tensor models, and sum-of-squares, in 2017 International Conference on Sampling Theory and Applications (SampTA), IEEE, 2017, pp. 124-128.
[174] W. Kim and F. Memoli, Stable Signatures for Dynamic Graphs and Dynamic Metric Spaces via Zigzag Persistence, preprint, https://arxiv.org/abs/1712.04064, 2017.
[175] W. Kim and F. Mémoli, Spatiotemporal persistent homology for dynamic metric spaces, Discrete Comput. Geom., 66 (2021), pp. 831-875. · Zbl 1480.55007
[176] W. Kim, F. Mémoli, and Z. Smith, Analysis of dynamic graphs and dynamic metric spaces via zigzag persistence, in Topological Data Analysis, Springer, 2020, pp. 371-389. · Zbl 1448.55008
[177] M. Kivelä, A. Arenas, M. Barthelemy, J. P. Gleeson, Y. Moreno, and M. A. Porter, Multilayer networks, J. Complex Networks, 2 (2014), pp. 203-271.
[178] S. Klamt, U.-U. Haus, and F. Theis, Hypergraphs and cellular networks, PLoS Comput. Biol., 5 (2009), art. e1000385.
[179] M. A. Komarov and A. Pikovsky, Finite-size-induced transitions to synchrony in oscillator ensembles with nonlinear global coupling, Phys. Rev. E, 92 (2015), art. 020901, https://doi.org/10.1103/PhysRevE.92.020901.
[180] N. Komodakis and N. Paragios, Beyond pairwise energies: Efficient optimization for higher-order MRFs, in 2009 IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2009, pp. 2985-2992.
[181] B. Kralemann, A. Pikovsky, and M. Rosenblum, Reconstructing effective phase connectivity of oscillator networks from observations, New J. Phys., 16 (2014), art. 085013, https://doi.org/10.1088/1367-2630/16/8/085013. · Zbl 1317.34057
[182] C. Kuehn and C. Bick, A universal route to explosive phenomena, Sci. Adv., 7 (2021), art. eabe3824, https://doi.org/10.1126/sciadv.abe3824.
[183] Y. Kuramoto, Chemical Oscillations, Waves, and Turbulence, Springer Ser. Synergetics 19, Springer, Berlin, 1984, https://doi.org/10.1007/978-3-642-69689-3. · Zbl 0558.76051
[184] R. Kwitt, S. Huber, M. Niethammer, W. Lin, and U. Bauer, Statistical topological data analysis: A kernel perspective, in Advances in Neural Information Processing Systems, 2015, pp. 3070-3078.
[185] R. Lambiotte, M. Rosvall, and I. Scholtes, From networks to optimal higher-order models of complex systems, Nature Phys., 15 (2019), pp. 313-320, https://doi.org/10.1038/s41567-019-0459-y.
[186] R. Lambiotte and M. Schaub, Modularity and Dynamics on Complex Networks, Cambridge University Press, 2021. · Zbl 1517.90022
[187] C. Lee and D. J. Wilkinson, A review of stochastic block models and extensions for graph clustering, Appl. Network Sci., 4 (2019), pp. 1-50.
[188] I. León and D. Pazó, Phase reduction beyond the first order: The case of the mean-field complex Ginzburg-Landau equation, Phys. Rev. E, 100 (2019), art. 012211, https://doi.org/10.1103/PhysRevE.100.012211.
[189] I. León and D. Pazó, Enlarged Kuramoto model: Secondary instability and transition to collective chaos, Phys. Rev. E, 105 (2022), art. L042201, https://doi.org/10.1103/PhysRevE.105.L042201.
[190] T. Lesieur, L. Miolane, M. Lelarge, F. Krzakala, and L. Zdeborová, Statistical and computational phase transitions in spiked tensor estimation, in 2017 IEEE International Symposium on Information Theory (ISIT), IEEE, 2017, pp. 511-515.
[191] M. Lesnick and M. Wright, Interactive Visualization of \textup2-D Persistence Modules, preprint, https://arxiv.org/abs/1512.00180, 2015.
[192] J. M. Levine, J. Bascompte, P. B. Adler, and S. Allesina, Beyond pairwise mechanisms of species coexistence in complex communities, Nature, 546 (2017), pp. 56-64, https://doi.org/10.1038/nature22898.
[193] P. Li and O. Milenkovic, Submodular hypergraphs: p-Laplacians, Cheeger inequalities and spectral clustering, in International Conference on Machine Learning, PMLR, 2018, pp. 3014-3023.
[194] L.-H. Lim, Hodge Laplacians on graphs, SIAM Rev., 62 (2020), pp. 685-715, https://doi.org/10.1137/18M1223101. · Zbl 1453.05061
[195] Y. Lin, S. Ren, C. Wang, and J. Wu, Weighted Path Homology of Weighted Digraphs and Persistence, preprint, https://arxiv.org/abs/1910.09891, 2019.
[196] N. Linial and R. Meshulam, Homological connectivity of random \(2\)-complexes, Combinatorica, 26 (2006), pp. 475-487. · Zbl 1121.55013
[197] M. Lucas, G. Cencetti, and F. Battiston, Multiorder Laplacian for synchronization in higher-order networks, Phys. Rev. Res., 2 (2020), art. 033410. · Zbl 1510.70065
[198] T. Łuczak and Y. Peled, Integral homology of random simplicial complexes, Discrete Comput. Geom., 59 (2018), pp. 131-142. · Zbl 1387.05275
[199] R. I. Lung, N. Gaskó, and M. A. Suciu, A hypergraph model for representing scientific output, Scientometrics, 117 (2018), pp. 1361-1379.
[200] D. Lütgehetmann, D. Govc, J. P. Smith, and R. Levi, Computing persistent homology of directed flag complexes, Algorithms, 13 (2020), art. 19.
[201] Y. Ma and Y. Fu, Manifold Learning Theory and Applications, CRC Press, 2011.
[202] S. A. Marvel, J. Kleinberg, R. D. Kleinberg, and S. H. Strogatz, Continuous-time model of structural balance, Proc. Natl. Acad. Sci. USA, 108 (2011), pp. 1771-1776.
[203] N. Masuda, M. A. Porter, and R. Lambiotte, Random walks and diffusion on networks, Phys. Rep., 716 (2017), pp. 1-58. · Zbl 1377.05180
[204] M. H. Matheny, J. Emenheiser, W. Fon, A. Chapman, A. Salova, M. Rohden, J. Li, M. Hudoba de Badyn, M. Pósfai, L. Duenas-Osorio, M. Mesbahi, J. P. Crutchfield, M. C. Cross, R. M. D’Souza, and M. L. Roukes, Exotic states in a simple network of nanoelectromechanical oscillators, Science, 363 (2019), art. eaav7932, https://doi.org/10.1126/science.aav7932.
[205] Y. Matsumoto, An Introduction to Morse Theory, Transl. Math. Monogr. 208, American Mathematical Society, 2002. · Zbl 0990.57001
[206] J. P. May, A Concise Course in Algebraic Topology, University of Chicago Press, 1999. · Zbl 0923.55001
[207] M. R. McGuirl, A. Volkening, and B. Sandstede, Topological data analysis of zebrafish patterns, Proc. Natl. Acad. Sci. USA, 117 (2020), pp. 5113-5124. · Zbl 1456.92004
[208] R.-M. Memmesheimer, Quantitative prediction of intermittent high-frequency oscillations in neural networks with supralinear dendritic interactions, Proc. Natl. Acad. Sci. USA, 107 (2010), pp. 11092-11097, https://doi.org/10.1073/pnas.0909615107.
[209] R.-M. Memmesheimer and M. Timme, Non-additive coupling enables propagation of synchronous spiking activity in purely random networks, PLoS Comput. Biol., 8 (2012), art. e1002384, https://doi.org/10.1371/journal.pcbi.1002384.
[210] R. Meshulam and N. Wallach, Homological connectivity of random k-dimensional complexes, Random Structures Algorithms, 34 (2009), pp. 408-417. · Zbl 1177.55011
[211] A. P. Millán, R. Ghorbanchian, N. Defenu, F. Battiston, and G. Bianconi, Local topological moves determine global diffusion properties of hyperbolic higher-order networks, Phys. Rev. E, 104 (2021), art. 054302, https://doi.org/10.1103/PhysRevE.104.054302.
[212] A. P. Millán, J. J. Torres, and G. Bianconi, Explosive higher-order Kuramoto dynamics on simplicial complexes, Phys. Rev. Lett., 124 (2020), art. 218301, https://doi.org/10.1103/PhysRevLett.124.218301.
[213] E. Miller, Data Structures for Real Multiparameter Persistence Modules, preprint, https://arxiv.org/abs/1709.08155, 2017.
[214] K. Mischaikow and M. Mrozek, Conley index, in Handbook of Dynamical Systems, Handb. Dynam. Syst. 2, Elsevier Science, 2002, pp. 393-460. · Zbl 1035.37010
[215] A. Muhammad and M. Egerstedt, Control using higher order Laplacians in network topologies, in Proceedings of the 17th International Symposium on Mathematical Theory of Networks and Systems, 2006, pp. 1024-1038.
[216] R. Mulas, C. Kuehn, and J. Jost, Coupled dynamics on hypergraphs: Master stability of steady states and synchronization, Phys. Rev. E, 101 (2020), art. 062313, https://doi.org/10.1103/PhysRevE.101.062313.
[217] E. Munch, Applications of Persistent Homology to Time Varying Systems, Doctoral dissertation, Duke University, Durham, NC, 2013.
[218] E. Munch, A user’s guide to topological data analysis, J. Learning Analytics, 4 (2017), pp. 47-61.
[219] J. R. Munkres, Elements of Algebraic Topology, CRC Press, 2018. · Zbl 0673.55001
[220] H. Nakao, Phase reduction approach to synchronisation of nonlinear oscillators, Contemp. Phys., 57 (2016), pp. 188-214, https://doi.org/10.1080/00107514.2015.1094987.
[221] J. T. Nardini, B. J. Stolz, K. B. Flores, H. A. Harrington, and H. M. Byrne, Topological data analysis distinguishes parameter regimes in the Anderson-Chaplain model of angiogenesis, PLoS Comput. Biol., 17 (2021), art. e1009094, https://doi.org/10.1371/journal.pcbi.1009094.
[222] R. Nenadov and N. Škorić, Powers of Hamilton cycles in random graphs and tight Hamilton cycles in random hypergraphs, Random Structures Algorithms, 54 (2019), pp. 187-208. · Zbl 1405.05167
[223] L. Neuhäuser, A. Mellor, and R. Lambiotte, Multibody interactions and nonlinear consensus dynamics on networked systems, Phys. Rev. E, 101 (2020), art. 032310.
[224] L. Neuhäuser, M. T. Schaub, A. Mellor, and R. Lambiotte, Opinion dynamics with multi-body interactions, in Network Games, Control and Optimization (NETGCOOP 2021) Springer, Cham, 2021, https://doi.org/10.1007/978-3-030-87473-5_23. · Zbl 1497.91242
[225] M. Newman, Networks, Oxford University Press, 2018. · Zbl 1391.94006
[226] M. Nicolau, A. J. Levine, and G. Carlsson, Topology based data analysis identifies a subgroup of breast cancers with a unique mutational profile and excellent survival, Proc. Natl. Acad. Sci. USA, 108 (2011), pp. 7265-7270.
[227] E. Nijholt and L. DeVille, Dynamical systems defined on simplicial complexes: Symmetries, conjugacies, and invariant subspaces, Chaos, 32 (2022), art. 093131, https://doi.org/10.1063/5.0093842. · Zbl 07878961
[228] E. Nijholt, B. Rink, and J. Sanders, Center manifolds of coupled cell networks, SIAM Rev., 61 (2019), pp. 121-155, https://doi.org/10.1137/18M1219977. · Zbl 1415.37034
[229] O. E. Omel’chenko, The mathematics behind chimera states, Nonlinearity, 31 (2018), pp. R121-R164, https://doi.org/10.1088/1361-6544/aaaa07. · Zbl 1395.34045
[230] A. Ortega, P. Frossard, J. Kovačević, J. M. Moura, and P. Vandergheynst, Graph signal processing: Overview, challenges, and applications, Proc. IEEE, 106 (2018), pp. 808-828.
[231] N. Otter, M. A. Porter, U. Tillmann, P. Grindrod, and H. A. Harrington, A roadmap for the computation of persistent homology, EPJ Data Sci., 6 (2017), art. 17.
[232] L. Page, S. Brin, R. Motwani, and T. Winograd, The PageRank Citation Ranking: Bringing Order to the Web, Tech. report, Stanford InfoLab, 1999.
[233] O. Parzanchevski and R. Rosenthal, Simplicial complexes: Spectrum, homology and random walks, Random Structures Algorithms, 50 (2017), pp. 225-261. · Zbl 1359.05114
[234] O. Parzanchevski, R. Rosenthal, and R. J. Tessler, Isoperimetric inequalities in simplicial complexes, Combinatorica, 36 (2016), pp. 195-227. · Zbl 1389.05174
[235] A. Patania, G. Petri, and F. Vaccarino, The shape of collaborations, EPJ Data Sci., 6 (2017), art. 18. · Zbl 1440.55004
[236] A. Patania, F. Vaccarino, and G. Petri, Topological analysis of data, EPJ Data Sci., 6 (2017), art. 7. · Zbl 1440.55004
[237] P. Pattison and G. Robins, Building models for social space: Neighourhood-based models for social networks and affiliation structures, Math. Sci. Hum. Math. Soc. Sci., 168 (2004), pp. 11-29. · Zbl 1127.91380
[238] L. M. Pecora and T. L. Carroll, Master stability functions for synchronized coupled systems, Phys. Rev. Lett., 80 (1998), pp. 2109-2112, https://doi.org/10.1103/PhysRevLett.80.2109.
[239] K. Pelka, V. Peano, and A. Xuereb, Chimera states in small optomechanical arrays, Phys. Rev. Res., 2 (2020), art. 013201, https://doi.org/10.1103/PhysRevResearch.2.013201.
[240] J. A. Perea, Topological time series analysis, Notices Amer. Math. Soc., 66 (2019), pp. 686-694. · Zbl 1416.37067
[241] B. Pietras and A. Daffertshofer, Network dynamics of coupled oscillators and phase reduction techniques, Phys. Rep., 819 (2019), pp. 1-105, https://doi.org/10.1016/j.physrep.2019.06.001.
[242] A. Pirayre, Y. Zheng, L. Duval, and J.-C. Pesquet, HOGMep: Variational Bayes and higher-order graphical models applied to joint image recovery and segmentation, in 2017 IEEE International Conference on Image Processing (ICIP), IEEE, 2017, pp. 3775-3779.
[243] R. Rabadán and A. J. Blumberg, Topological Data Analysis for Genomics and Evolution: Topology in Biology, Cambridge University Press, 2019. · Zbl 1462.92001
[244] J. Ray and M. Trovati, A survey of topological data analysis (TDA) methods implemented in Python, in International Conference on Intelligent Networking and Collaborative Systems, Springer, 2017, pp. 594-600.
[245] Y. Reani and O. Bobrowski, Cycle Registration in Persistent Homology with Applications in Topological Bootstrap, preprint, https://arxiv.org/abs/2101.00698, 2021.
[246] M. W. Reimann, M. Nolte, M. Scolamiero, K. Turner, R. Perin, G. Chindemi, P. Dłotko, R. Levi, K. Hess, and H. Markram, Cliques of neurons bound into cavities provide a missing link between structure and function, Frontiers Comput. Neurosci., 11 (2017), art. 48.
[247] M. Reitz and G. Bianconi, The higher-order spectrum of simplicial complexes: A renormalization group approach, J. Phys. A, 53 (2020), art. 295001, https://doi.org/10.1088/1751-8121/ab9338. · Zbl 1519.82042
[248] S. Ren, Persistent homology for hypergraphs and computational tools: A survey for users, J. Knot Theory Ramifications, 29 (2020), art. 2043007. · Zbl 1460.55008
[249] V. Robins, J. D. Meiss, and E. Bradley, Computing connectedness: An exercise in computational topology, Nonlinearity, 11 (1998), pp. 913-922. · Zbl 0957.54010
[250] A. Robinson and K. Turner, Hypothesis testing for topological data analysis, J. Appl. Comput. Topol., 1 (2017), pp. 241-261. · Zbl 1396.62085
[251] M. Robinson, Hunting for foxes with sheaves, Notices Amer. Math. Soc., 66 (2019), pp. 661-676. · Zbl 1422.94016
[252] T. M. Roddenberry, M. T. Schaub, and M. Hajij, Signal processing on cell complexes, in ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, 2022, pp. 8852-8856.
[253] T. M. Roddenberry and S. Segarra, HodgeNet: Graph neural networks for edge data, in 2019 53rd Asilomar Conference on Signals, Systems, and Computers, IEEE, 2019, pp. 220-224.
[254] F. A. Rodrigues, T. K. D. Peron, P. Ji, and J. Kurths, The Kuramoto model in complex networks, Phys. Rep., 610 (2016), pp. 1-98, https://doi.org/10.1016/j.physrep.2015.10.008. · Zbl 1357.34089
[255] M. Saadatfar, H. Takeuchi, V. Robins, N. Francois, and Y. Hiraoka, Pore configuration landscape of granular crystallization, Nature Commun., 8 (2017), pp. 1-11.
[256] A. Salova and R. M. D’Souza, Analyzing States beyond Full Synchronization on Hypergraphs Requires Methods beyond Projected Networks, preprint, http://arxiv.org/abs/2107.13712, 2021.
[257] A. Salova and R. M. D’Souza, Cluster Synchronization on Hypergraphs, preprint, http://arxiv.org/abs/2101.05464, 2021.
[258] S. Scaramuccia, F. Iuricich, L. De Floriani, and C. Landi, Computing multiparameter persistent homology through a discrete Morse-based approach, Comput. Geom., 89 (2020), art. 101623. · Zbl 1479.55014
[259] S. H. Schanuel, What is the length of a potato?, in Categories in Continuum Physics, Springer, 1986, pp. 118-126. · Zbl 0621.51023
[260] M. T. Schaub, A. R. Benson, P. Horn, G. Lippner, and A. Jadbabaie, Random walks on simplicial complexes and the normalized Hodge \(1\)-Laplacian, SIAM Rev., 62 (2020), pp. 353-391, https://doi.org/10.1137/18M1201019. · Zbl 1441.05205
[261] M. T. Schaub, J.-C. Delvenne, M. Rosvall, and R. Lambiotte, The many facets of community detection in complex networks, Appl. Network Sci., 2 (2017), art. 4.
[262] M. T. Schaub, J.-B. Seby, F. Frantzen, T. M. Roddenberry, Y. Zhu, and S. Segarra, Signal processing on simplicial complexes, in Higher-Order Systems, Springer, 2022, pp. 301-328. · Zbl 1510.94063
[263] M. T. Schaub and S. Segarra, Flow smoothing and denoising: Graph signal processing in the edge-space, in 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), IEEE, 2018, pp. 735-739.
[264] M. T. Schaub, Y. Zhu, J.-B. Seby, T. M. Roddenberry, and S. Segarra, Signal processing on higher-order networks: Livin’ on the edge... and beyond, Signal Process., 187 (2021), art. 108149.
[265] H. Schenck, Algebra and Topology for Data Analysis, 2020, http://webhome.auburn.edu/ hks0015/AuburnTDA.html.
[266] J. Schmidt-Pruzan and E. Shamir, Component structure in the evolution of random hypergraphs, Combinatorica, 5 (1985), pp. 81-94. · Zbl 0573.05042
[267] Y. Shemesh, Y. Sztainberg, O. Forkosh, T. Shlapobersky, A. Chen, and E. Schneidman, High-order social interactions in groups of mice, eLife, 2 (2013), art. e00759.
[268] D. I. Shuman, S. K. Narang, P. Frossard, A. Ortega, and P. Vandergheynst, The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains, IEEE Signal Process. Mag., 30 (2013), pp. 83-98.
[269] J. Silva and R. Willett, Hypergraph-based anomaly detection of high-dimensional co-occurrences, IEEE Trans. Pattern Anal. Mach. Intell., 31 (2008), pp. 563-569.
[270] A. E. Sizemore, J. E. Phillips-Cremins, R. Ghrist, and D. S. Bassett, The importance of the whole: Topological data analysis for the network neuroscientist, Network Neurosci., 3 (2019), pp. 656-673.
[271] P. S. Skardal and A. Arenas, Abrupt desynchronization and extensive multistability in globally coupled oscillator simplexes, Phys. Rev. Lett., 122 (2019), art. 248301, https://doi.org/10.1103/PhysRevLett.122.248301.
[272] P. S. Skardal and A. Arenas, Higher order interactions in complex networks of phase oscillators promote abrupt synchronization switching, Commun. Phys., 3 (2020), art. 218, https://doi.org/10.1038/s42005-020-00485-0.
[273] J. Skvoretz and K. Faust, Logit models for affiliation networks, Sociological Methodology, 29 (1999), pp. 253-280.
[274] C. Spagnuolo, G. Cordasco, P. Szufel, P. Prałat, V. Scarano, B. Kamiński, and A. Antelmi, Analyzing, exploring, and visualizing complex networks via hypergraphs using SimpleHypergraphs.jl, Internet Math., 1 (2020), art. 12464.
[275] T. Stankovski, T. Pereira, P. V. E. McClintock, and A. Stefanovska, Coupling functions: Universal insights into dynamical interaction mechanisms, Rev. Modern Phys., 89 (2017), art. 045001, https://doi.org/10.1103/RevModPhys.89.045001.
[276] D. Stasi, K. Sadeghi, A. Rinaldo, S. Petrović, and S. E. Fienberg, Beta models for random hypergraphs with a given degree sequence, in Proceedings of 21st International Conference on Computational Statistics, 2014. · Zbl 1436.91097
[277] I. Stewart, M. Golubitsky, and M. Pivato, Symmetry groupoids and patterns of synchrony in coupled cell networks, SIAM J. Appl. Dyn. Syst., 2 (2003), pp. 609-646, https://doi.org/10.1137/S1111111103419896. · Zbl 1089.34032
[278] B. Stolz, Computational Topology in Neuroscience, Master’s thesis, University of Oxford, 2014.
[279] B. J. Stolz, H. A. Harrington, and M. A. Porter, Persistent homology of time-dependent functional networks constructed from coupled time series, Chaos, 27 (2017), art. 047410.
[280] B. J. Stolz, J. Kaeppler, B. Markelc, F. Mech, F. Lipsmeier, R. J. Muschel, H. M. Byrne, and H. A. Harrington, Multiscale Topology Characterises Dynamic Tumour Vascular Networks, preprint, https://arxiv.org/abs/2008.08667, 2020.
[281] B. Stolz-Pretzer, Global and Local Persistent Homology for the Shape and Classification of Biological Data, Ph.D. thesis, University of Oxford, 2019.
[282] S. H. Strogatz, From Kuramoto to Crawford: Exploring the onset of synchronization in populations of coupled oscillators, Phys. D, 143 (2000), pp. 1-20, https://doi.org/10.1016/S0167-2789(00)00094-4. · Zbl 0983.34022
[283] S. H. Strogatz, Sync: The Emerging Science of Spontaneous Order, Penguin, 2004.
[284] A. Tahbaz-Salehi and A. Jadbabaie, Distributed coverage verification in sensor networks without location information, IEEE Trans. Automat. Control, 55 (2010), pp. 1837-1849. · Zbl 1368.90039
[285] T. Tanaka and T. Aoyagi, Multistable attractors in a network of phase oscillators with three-body interactions, Phys. Rev. Lett., 106 (2011), art. 224101, https://doi.org/10.1103/PhysRevLett.106.224101.
[286] D. Taylor, F. Klimm, H. A. Harrington, M. Kramár, K. Mischaikow, M. A. Porter, and P. J. Mucha, Topological data analysis of contagion maps for examining spreading processes on networks, Nature Commun., 6 (2015), pp. 1-11.
[287] J. B. Tenenbaum, V. De Silva, and J. C. Langford, A global geometric framework for nonlinear dimensionality reduction, Science, 290 (2000), pp. 2319-2323.
[288] M. Timme and J. Casadiego, Revealing networks from dynamics: An introduction, J. Phys. A, 47 (2014), art. 343001, https://doi.org/10.1088/1751-8113/47/34/343001. · Zbl 1305.92036
[289] C. M. Topaz, L. Ziegelmeier, and T. Halverson, Topological data analysis of biological aggregation models, PloS One, 10 (2015), art. e0126383.
[290] L. Torres, A. S. Blevins, D. Bassett, and T. Eliassi-Rad, The why, how, and when of representations for complex systems, SIAM Rev., 63 (2021), pp. 435-485, https://doi.org/10.1137/20M1355896. · Zbl 1470.00003
[291] F. Tudisco and D. J. Higham, Node and edge nonlinear eigenvector centrality for hypergraphs, Commun. Phys., 4 (2021), art. 201, https://doi.org/10.1038/s42005-021-00704-2.
[292] S. Tymochko, E. Munch, and F. A. Khasawneh, Using zigzag persistent homology to detect Hopf bifurcations in dynamical systems, Algorithms (Basel), 13 (2020), art. 278.
[293] L. R. Varshney, B. L. Chen, E. Paniagua, D. H. Hall, and D. B. Chklovskii, Structural properties of the Caenorhabditis elegans neuronal network, PLoS Comput. Biol., 7 (2011), art. e1001066.
[294] A. Vazquez, Population stratification using a statistical model on hypergraphs, Phys. Rev. E, 77 (2008), art. 066106.
[295] A. Vazquez, Finding hypergraph communities: A Bayesian approach and variational solution, J. Statist. Mech. Theory Exp., 2009 (2009), art. P07006.
[296] N. Veldt, A. R. Benson, and J. Kleinberg, Hypergraph Cuts with General Splitting Functions, preprint, https://arxiv.org/abs/2001.02817, 2020. · Zbl 1494.05080
[297] O. Vipond, J. A. Bull, P. S. Macklin, U. Tillmann, C. W. Pugh, H. M. Byrne, and H. A. Harrington, Multiparameter persistent homology landscapes identify immune cell spatial patterns in tumors, Proc. Natl. Acad. Sci. USA, 118 (2021), art. e2102166118.
[298] S. T. Vittadello and M. P. Stumpf, Model comparison via simplicial complexes and persistent homology, Roy. Soc. Open Sci., 8 (2021), art. 211361, https://doi.org/10.1098/rsos.211361.
[299] T. R. Walsh, Hypermaps versus bipartite maps, J. Combin. Theory Ser. B, 18 (1975), pp. 155-163, https://doi.org/10.1016/0095-8956(75)90042-8. · Zbl 0302.05101
[300] P. Wang, K. Sharpe, G. L. Robins, and P. E. Pattison, Exponential random graph \((p*)\) models for affiliation networks, Social Networks, 31 (2009), pp. 12-25.
[301] L. Wasserman, Topological data analysis, Annu. Rev. Stat. Appl., 5 (2018), pp. 501-532.
[302] K. Xia and G.-W. Wei, Persistent homology analysis of protein structure, flexibility, and folding, Int. J. Numer. Methods Biomed. Engrg., 30 (2014), pp. 814-844.
[303] L. Xian, H. Adams, C. M. Topaz, and L. Ziegelmeier, Capturing Dynamics of Time-Varying Data via Topology, preprint, https://arxiv.org/abs/2010.05780, 2020. · Zbl 1501.37084
[304] G. Yan, P. E. Vértes, E. K. Towlson, Y. L. Chew, D. S. Walker, W. R. Schafer, and A.-L. Barabási, Network control principles predict neuron function in the Caenorhabditis elegans connectome, Nature, 550 (2017), pp. 519-523.
[305] M. Yang, E. Isufi, M. T. Schaub, and G. Leus, Finite impulse response filters for simplicial complexes, in 2021 29th European Signal Processing Conference (EUSIPCO), IEEE, 2021, pp. 2005-2009.
[306] M. Yang, E. Isufi, M. T. Schaub, and G. Leus, Simplicial convolutional filters, IEEE Trans. Signal Process., 70 (2022), pp. 4633-4648, https://doi.org/10.1109/TSP.2022.3207045. · Zbl 07911564
[307] X. Yang, Social Network Modeling and the Evaluation of Structural Similarity for Community Detection, Ph.D. thesis, Carnegie Mellon University, 2015.
[308] J. R. Yaros and T. Imielinski, Imbalanced hypergraph partitioning and improvements for consensus clustering, in 2013 IEEE 25th International Conference on Tools with Artificial Intelligence, IEEE, 2013, pp. 358-365.
[309] J.-G. Young, G. Petri, F. Vaccarino, and A. Patania, Construction of and efficient sampling from the simplicial configuration model, Phys. Rev. E, 96 (2017), art. 032312. · Zbl 1440.55004
[310] E. C. Zeeman, The topology of the brain and visual perception, in Topology of 3-Manifolds and Related Topics, Prentice-Hall, Englewood Cliffs, NJ, 1962, pp. 240-256. · Zbl 1246.92006
[311] S. Zhang, Z. Ding, and S. Cui, Introducing hypergraph signal processing: Theoretical foundation and practical applications, IEEE Internet Things J., 7 (2019), pp. 639-660.
[312] Z.-K. Zhang and C. Liu, A hypergraph model of social tagging networks, J. Statist. Mech. Theory Exp., 2010 (2010), art. P10005.
[313] E. Zheleva, L. Getoor, and S. Sarawagi, Higher-order graphical models for classification in social and affiliation networks, in NIPS Workshop on Networks Across Disciplines: Theory and Applications, Vol. 2, 2010.
[314] D. Zhou, J. Huang, and B. Schölkopf, Learning with hypergraphs: Clustering, classification, and embedding, Adv. Neural Inform. Process. Syst., 19 (2006).
[315] A. Zomorodian and G. Carlsson, Computing persistent homology, Discrete Comput. Geom., 33 (2005), pp. 249-274. · Zbl 1069.55003
[316] K. Zuev, O. Eisenberg, and D. Krioukov, Exponential random simplicial complexes, J. Phys. A, 48 (2015), art. 465002. · Zbl 1331.90020
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.