The DOI matches its Crossref record; 304 of the 316 references it cites were found in a registry or an index.
What Are Higher-Order Networks?
Open accessAbstract
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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Additional complexity stems from the fact that those interactions are typically not of a pairwise nature, but rather form higher-order relationships -the simplest example being a passenger connecting multiple flights, thus creating a relation between many elements simultaneously. This could be tackled by the recent concept of higher-order networks ( Bianconi, 2021; Bick et al., 2023 ).”
From Status quo and challenges in air transport management research (2024) · cites this paper as Bick et al., 2023
Citations
Cites
A Global Geometric Framework for Nonlinear Dimensionality Reduction
Tenenbaum, J. B., Silva, V. D., & Langford, J. C. (2000) · Science · doi:10.1126/science.290.5500.2319 · Cited by 13,585
Complex networks: structure and dynamics
BOCCALETTI, S., LATORA, V, MORENO, Y., CHAVEZ, M., & HWANG, D. (2006) · Physics Reports · doi:10.1016/j.physrep.2005.10.009 · Cited by 11,010
Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
Belkin, M., & Niyogi, P. (2003) · Neural Computation · doi:10.1162/089976603321780317 · Cited by 7,562
Chemical Oscillations, Waves, and Turbulence
Kuramoto, Y. (1984) · Springer series in synergetics · doi:10.1007/978-3-642-69689-3 · Cited by 7,242
Chung, F. (1996) · Regional conference series in mathematics · doi:10.1090/cbms/092 · Cited by 5,918
(2001) · Cited by 5,374
Shuman, D. I., Narang, S. K., Frossard, P., Ortega, A., & Vandergheynst, P. (2013) · IEEE Signal Processing Magazine · doi:10.1109/msp.2012.2235192 · Cited by 4,421
The Kuramoto model: A simple paradigm for synchronization phenomena
Acebrón, J. A., Bonilla, L. L., Vicente, C. J. P., Ritort, F., & Spigler, R. (2005) · Reviews of Modern Physics · doi:10.1103/revmodphys.77.137 · Cited by 3,579
Kivela, M., Arenas, A., Barthelemy, M., Gleeson, J. P., Moreno, Y., & Porter, M. A. (2014) · Journal of Complex Networks · doi:10.1093/comnet/cnu016 · Cited by 3,235
Strogatz, S. H. (2000) · Physica D Nonlinear Phenomena · doi:10.1016/s0167-2789(00)00094-4 · Cited by 3,094
Flagged first, then by citations
12 of the 316 references couldn’t be matched to a known paper.
Research works printed but not linked: 49
- Adams H., 2017, J. Mach. Learn. Res., 18, 8
- Aukerman A., 2020, 36th International Symposium on Computational Geometry (SoCG 2020
- Benson A. R., 1974, Proceedings of the 2015 SIAM International Conference on Data Mining, SIAM, 2015, 118
- Bick C., Phase oscillator networks with nonlocal higher-order interactions: Twisted states, stability, and bifurcations
- Bick C., 2023, 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 · DOI
- Bubenik P., 2015, J. Mach. Learn. Res., 16, 77
- Byrne H. M., 2019, Math. Today (Southend-on-Sea), 55, 206
- Chaplin T., 2021, First Betti Number of the Path Homology of Random Directed Graphs, preprint, https://arxiv.org/abs/2111.13493 · arXiv
- Chazal F., 2015, International Conference on Machine Learning, PMLR, 2143
- Chazal F., 2021, Frontiers Artificial Intell., 4
- Chen Y.-C., 2021, Helmholtzian Eigenmap: Topological Feature Discovery & Edge Flow Learning from Point Cloud Data, preprint, https://arxiv.org/abs/2103.07626 · arXiv
- Chien I., 2018, International Conference on Artificial Intelligence and Statistics, 871
- Chowdhury S., 2020, International Conference on Complex Networks and Their Applications, 639
- Chowdhury S., 1975, Proceedings of the Twenty-Ninth Annual ACM-SIAM Symposium on Discrete Algorithms, SIAM, 2018, 1152
- Chung F. R., 1997, CBMS Reg. Conf. Ser. Math. 92
- De Silva V., 2007, Notices Amer. Math. Soc., 54, 10
- DeVille L., 2020, Consensus on Simplicial Complexes, or: The Nonlinear Simplicial Laplacian, preprint, https://arxiv.org/abs/2010.07421 · arXiv
- 10.1017/CBO9780511761942 · DOI · Paper page
- Farber M., 2019, Random Simplicial Complexes, Duality and the Critical Dimension, preprint, https://arxiv.org/abs/1901.09578 · arXiv
- Ghoshdastidar D., 2014, Advances in Neural Information Processing Systems, 397
- Glaze N., 2021, Principled Simplicial Neural Networks for Trajectory Prediction, preprint, https://arxiv.org/abs/2102.10058 · arXiv
- Gross E., 2021, Goodness-of-Fit Tests, and Applications to Biological Networks, preprint, https://arxiv.org/abs/2104.03167 · arXiv
- Harer J., 2015, Tech. report
- Hein M., 2013, Adv. Neural Inform. Process. Syst., 26
- Helm A., 2020, The Growing Topology of the C. Elegans Connectome, preprint, https://arxiv.org/abs/2101.00065 · arXiv
- Hickok A., 2021, A Bounded-Confidence Model of Opinion Dynamics on Hypergraphs, preprint, https://arxiv.org/abs/2102.06825 · arXiv
- Kaczynski T., 2006, Appl. Math. Sci. 157
- Kahle M., 2014, American Mathematical Society, 201
- Ke Z. T., 2019, Community Detection for Hypergraph Networks via Regularized Tensor Power Iteration, preprint, https://arxiv.org/abs/1909.06503 · arXiv
- Kerber M., 1976, 2021 Proceedings of the Workshop on Algorithm Engineering and Experiments (ALENEX), SIAM, 2021, 207
- Kwitt R., 2015, Advances in Neural Information Processing Systems, 3070
- Li P., 2018, International Conference on Machine Learning, PMLR, 3014
- Matsumoto Y., 2002, Transl. Math. Monogr. 208
- Neuhäuser L., 2021, Control and Optimization (NETGCOOP 2021)
- 10.1063/5.0093842 · DOI
- 10.1137/18M1219977 · DOI
- Page L., 1999, Tech. report
- Pattison P., 2004, Math. Sci. Hum. Math. Soc. Sci., 168, 11
- 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. · arXiv
- A. Salova and R. M. D'Souza, Cluster Synchronization on Hypergraphs , preprint,http://arxiv.org/abs/2101.05464, 2021. · arXiv
- Schenck H., 2020, Algebra and Topology for Data Analysis
- Spagnuolo C., 2020, Internet Math., 1, 12464
- Stasi D., 2014, Proceedings of 21st International Conference on Computational Statistics
- Stolz B., 2014, Master's thesis
- Taylor D., 2015, Nature Commun., 6, 1
- Veldt N., 2020, Hypergraph Cuts with General Splitting Functions, preprint, https://arxiv.org/abs/2001.02817 · arXiv
- X. Yang, Social Network Modeling and the Evaluation of Structural Similarity for Community Detection , Ph.D. thesis, Carnegie Mellon University, 2015.
- Zheleva E., 2010, NIPS Workshop on Networks Across Disciplines: Theory and Applications, 2
- Zhou D., 2006, Adv. Neural Inform. Process. Syst., 19
Cited by
Sorted by citations
Items 201–300 of 426
DISTRIBUTED INTERVAL CONSENSUS IN NETWORKED SYSTEMS WITH PRIVACY PRESERVATION
Shang, Y. (2026) · The ANZIAM Journal · doi:10.1017/s1446181126100303 · Cited by 3
Stochastic resonance in higher-order networks driven by colored noise
Bi, Z., Zhao, D., Liu, Q., Kurths, J. U., & Xu, Y. (2026) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/5.0327064 · Cited by 3
Gao, C., Jiang, H., & Guo, X. (2024) · International Journal of Disaster Risk Reduction · doi:10.1016/j.ijdrr.2024.104747 · Cited by 3
Higher-order interactions lead to ‘reluctant’ synchrony breaking
von der Gracht, S., Nijholt, E., & Rink, B. (2024) · Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rspa.2023.0945 · Cited by 3
Hypercurveball algorithm for sampling hypergraphs with fixed degrees
Kraakman, Y. J., & Stegehuis, C. (2025) · Journal of Complex Networks · doi:10.1093/comnet/cnaf007 · Cited by 3
The Reconfiguration of Hebrew in the Hellenistic Period
Coeckelbergs, M. (2018) · BRILL eBooks · doi:10.1163/9789004366770 · Cited by 3
Timonidis, N., Bakker, R., Rubio-Teves, M., Alonso-Martínez, C., García‐Amado, M., Clascá, F., & Tiesinga, P. H. E. (2023) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2023.1272243 · Cited by 4
A fast algorithm for diffusion source localization in large-scale complex networks
Pan, C., Wang, J., Di, Y., Zhang, C., & Zhang, X. (2024) · Journal of Complex Networks · doi:10.1093/comnet/cnae014 · Cited by 3
Higher-Order Null Models as a Lens for Social Systems
Preti, G., Fazzone, A., Petri, G., & Morales, G. D. F. (2024) · Physical Review X · doi:10.1103/physrevx.14.031032 · Cited by 3
Nonlinear diffusion on networks: Perturbations and consensus dynamics
Bonetto, R., & Jardón-Kojakhmetov, H. (2024) · Networks and Heterogeneous Media · doi:10.3934/nhm.20240058 · Cited by 3
Perotti, J. I. (2025) · Physical review. E · doi:10.1103/physreve.111.034306 · Cited by 3
Combined higher-order interactions of mixed symmetry on the sphere
Lohe, M. A. (2022) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/5.0079696 · Cited by 3
Heteroclinic dynamics in network dynamical systems with higher-order interactions
Bick, C., & von der Gracht, S. (2024) · Journal of Complex Networks · doi:10.1093/comnet/cnae009 · Cited by 3
Self-citationVlasov equations on directed hypergraph measures
Kuehn, C., & Xu, C. (2025) · Partial Differential Equations and Applications · doi:10.1007/s42985-025-00313-6 · Cited by 3
A tutorial on networks of social systems: A mathematical modeling perspective
Brooks, H. Z. (2025) · Proceedings of symposia in applied mathematics · doi:10.1090/psapm/080/00730 · Cited by 3
AHD-SLE: Anomalous Hyperedge Detection on Hypergraph Symmetric Line Expansion
Li, Y., Yu, H., Li, H., Pan, F., & Liu, S. (2024) · Axioms · doi:10.3390/axioms13060387 · Cited by 2
Chen, Y., Ng, H. K. T., Gel, Y. R., & Poor, H. V. (2024) · Journal of the Royal Statistical Society Series C (Applied Statistics) · doi:10.1093/jrsssc/qlae071 · Cited by 2
Beyond trivial edges: A fractional approach to cohesive subgraph detection in hypergraphs
Kim, H., Shin, W., Kim, J., Kim, J., Lim, S., & Jeong, H. J. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113472 · Cited by 2
曹, W.-X. 文. C., Wang, M., Xu, F., 黄, S.-F. 守. H., & Zhang, J. (2025) · Chinese Physics B · doi:10.1088/1674-1056/adfc41 · Cited by 2
Beyond Pairwise Network Interactions: Implications for Information Centrality
Lera, S. C., & Leng, Y. (2025) · Information Systems Research · doi:10.1287/isre.2024.1097 · Cited by 2
Sato, G., Nakao, H., & Muolo, R. (2026) · The European Physical Journal Special Topics · doi:10.1140/epjs/s11734-026-02132-1 · Cited by 2
Aste, T. (2025) · Journal of Physics Complexity · doi:10.1088/2632-072x/ae28c3 · Cited by 2
Indirect influence on network diffusion
Torres-Hugas, L., Duch, J., & Gómez, S. (2025) · Communications Physics · doi:10.1038/s42005-025-02403-8 · Cited by 2
Pattern dynamics of network information propagation model with higher-order interactions
Hu, K., & Zhu, L. (2025) · Information Sciences · doi:10.1016/j.ins.2025.122812 · Cited by 2
Emergence of temporal higher-order interactions from pairwise collaboration
Hao, Y., Liu, J., Wang, J., & Zheng, Z. M. (2026) · Communications Physics · doi:10.1038/s42005-025-02463-w · Cited by 2
Liu, Y., Wang, Q., Chen, W., & Shen, T. (2026) · Complex & Intelligent Systems · doi:10.1007/s40747-025-02225-9 · Cited by 3
Random walks across dimensions: Exploring simplicial complexes
Febbe, D., Fanelli, D., & Carletti, T. (2026) · Physical review. E · doi:10.1103/ppm1-mxhz · Cited by 2
Shajahan, D. (2026) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2026.117972 · Cited by 2
Namura, N., Muolo, R., & Nakao, H. (2026) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/5.0307452 · Cited by 2
Opinion Dynamics on Higher-Order Social Networks: A Continuous-Time Perspective
Duan, Z., Yan, J., Dong, X., Liu, F., & Tang, Y. (2026) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2026.3686176 · Cited by 2
Organic Chemistry as a Catalyst for AI Innovation: Challenges, Methods, and Emerging Paradigms
Chawla, N. V., González‐Montiel, G. A., Guo, K., Guo, T., Hua, T., Huang, X., Inae, E., Jiang, M., Le, K., Liu, G., et al. (2026) · Chemical Reviews · doi:10.1021/acs.chemrev.5c01081 · Cited by 4
Influence of excitatory and inhibitory higher-order chemical coupling on spiral waves dynamics
Rajagopal, S., Rajagopal, K., Thoai, V. P., Tlelo‐Cuautle, E., & Jafari, S. (2026) · The European Physical Journal Special Topics · doi:10.1140/epjs/s11734-026-02427-3 · Cited by 2
Montanari, A. N., Zanin, P., & Motter, A. E. (2026) · Science · doi:10.1126/science.aeg3946 · Cited by 3
TOPO-MLP : A Simplicial Network without Message Passing
Ramamurthy, K. N., Guzmán-Sáenz, A., & Hajij, M. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10094803 · Cited by 2
Predicting hyperlinks via weighted hypernetwork loop structure
Peng, H., Li, S., Zhao, D., Zhong, M. J., Qian, C., & Wang, W. (2024) · The European Physical Journal Special Topics · doi:10.1140/epjs/s11734-024-01145-y · Cited by 2
Learning the Topology of a Simplicial Complex Using Simplicial Signals: A Greedy Approach
Buciulea, A., Isufi, E., Leus, G. J. T., & Marques, A. G. (2024) · IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM) · doi:10.1109/sam60225.2024.10636498 · Cited by 2
Liu, H., Shen, D., Dabić, M., & Lu, J. (2025) · IEEE Transactions on Engineering Management · doi:10.1109/tem.2025.3534687 · Cited by 2
Zino, L., & Rizzo, A. (2024) · IEEE Conference on Decision and Control (CDC) · doi:10.1109/cdc56724.2024.10886121 · Cited by 2
Resilience patterns in higher-order meta-population networks
Nie, Y., Liu, Y., Cao, Q., (124891), T. L., & Wang, W. (2026) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2026.112650 · Cited by 2
Topological data analysis of truncated contagion maps
Klimm, F. (2022) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/5.0090114 · Cited by 2
Higher-order network information propagation model based on social impact theory
Liu, X., Li, R., Song, Y.-R., Qu, H., & Jiang, G. (2024) · Physics Letters A · doi:10.1016/j.physleta.2024.129969 · Cited by 2
Topological signal processing on quantum computers for higher-order network analysis
Leditto, C. M. G., Southwell, A., Tonekaboni, B., White, G. A. L., Usman, M., & Modi, K. K. (2025) · Physical Review Applied · doi:10.1103/physrevapplied.23.054054 · Cited by 2
Effects of higher-order interactions and homophily on information access inequality
Laber, M., Dies, S., Ehlert, J., Klein, B., & Eliassi‐Rad, T. (2025) · Communications Physics · doi:10.1038/s42005-025-02445-y · Cited by 2
Distinguishing pairwise and higher-order interactions in coupled oscillators from time series
Su, W., Hata, S., Kori, H., Nakao, H., & Kobayashi, R. (2025) · Communications Physics · doi:10.1038/s42005-025-02467-6 · Cited by 2
The frequency response of networks as open systems
Nazerian, A., Asllani, M., Tyloo, M., Ku, W. L., & Sorrentino, F. (2026) · Nature Communications · doi:10.1038/s41467-026-68602-1 · Cited by 4
Arefev, A. M., Grines, E. A., & Osipov, G. V. (2023) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/5.0156446 · Cited by 2
Impacts of link removal on the synchronization of higher-order networks
Chen, L., Natiq, H., Parastesh, F., & Hussain, I. (2024) · The European Physical Journal Special Topics · doi:10.1140/epjs/s11734-024-01157-8 · Cited by 2
Hypergraph Unreliability in Quasi-Polynomial Time
Cen, R., Li, J., & Panigrahi, D. (2024) · ACM Symposium on Theory of Computing (STOC) · doi:10.1145/3618260.3649753 · Cited by 2
Inference of time-ordered multibody interactions
Alvarez-Rodriguez, U., Petrović, L. V., & Scholtes, I. (2023) · Physical review. E · doi:10.1103/physreve.108.034312 · Cited by 2
Malik, M. G. A., Bashir, Z., Akhtar, M. W., & Awais, M. (2024) · Research Square · doi:10.21203/rs.3.rs-4189997/v1 · Cited by 2
Classifying the Aramaic Texts from Qumran A Statistical Analysis of Linguistic Features
Starr, J. (2016) · Bloomsbury Publishing Plc eBooks · doi:10.5040/9780567667847 · Cited by 1
Performance of higher-order networks in reconstructing sequential paths: from micro to macro scale
Teo, K., Arnold, N. A., Hone, A. N. W., & Kiss, I. Z. (2024) · Journal of Complex Networks · doi:10.1093/comnet/cnae050 · Cited by 1
Dshgnn: dual-channel signed hypergraph neural network for link sign prediction
Chen, Y., Yu, T., & Zhang, Q. (2025) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-025-02659-0 · Cited by 1
Control of chimera states via adaptive higher-order interactions
Andreev, A. V., Badarin, A., Ghosh, D., Николаевна, П. Е., & Hramov, A. E. (2025) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/5.0296464 · Cited by 1
Zino, L., & Rizzo, A. (2025) · IEEE Transactions on Control of Network Systems · doi:10.1109/tcns.2025.3629505 · Cited by 1
Cinardi, N., & Bertotti, M. L. (2025) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2025.117584 · Cited by 1
Matched Topological Subspace Detector
Liu, C. J., Tenorio, V. M., Marques, A. G., & Isufi, E. (2026) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2026.3656668 · Cited by 1
Kanagaraj, S., Durairaj, P., & Zheng, Z. (2026) · Nonlinear Dynamics · doi:10.1007/s11071-025-12074-3 · Cited by 1
Wang, C., & Li, N. (2026) · Physica Scripta · doi:10.1088/1402-4896/ae41f2 · Cited by 1
Symmetry prior based reconstruction of higher-order networks from time-series data
Yan, M., Zhang, H., Wang, H., Ma, C., & Chen, G. (2026) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/5.0314521 · Cited by 1
High-Order Interactions Reshape the Carbon Emission Efficiency Network Across Chinese Regions
Du, R., Ge, X., Kong, Z., Shi, Q., Ahsan, M., & Tian, L. (2026) · Entropy · doi:10.3390/e28040431 · Cited by 1
Opinion modulation and higher-order contact effects in dual-virus epidemic dynamics
Wen, K., Zhai, S., Wang, H., & Tao, J. (2026) · Nonlinear Dynamics · doi:10.1007/s11071-026-12493-w · Cited by 1
Analyzing Higher-Order Homophily of Category Pairs in Temporal Hypergraphs
Nishimura, K., Kumano, M., & Kimura, M. (2026) · Studies in computational intelligence · doi:10.1007/978-3-032-16649-4_26 · Cited by 1
Recursive simplicial networks: Degree characteristics and Zagreb index analysis
Irshad, S., Ali, Y., & Shang, Y. (2026) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2026.118561 · Cited by 1
Persistent homology filtration grounded on higher-order complex networks centrality measures
Raj, U., Maletić, S., & Bhattacharya, S. (2026) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/5.0331377 · Cited by 1
Epidemic spreading driven by L -step random walks with stochastic resetting
Li, M., Zhu, F., & Xiong, X. (2026) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/5.0323553 · Cited by 1
Zhong, Y., Ding, C., & Chen, X. (2026) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/5.0351862 · Cited by 1
Exploring the space of graphs with fixed discrete curvatures
Roost, M., Devriendt, K., Zucal, G., & Jost, J. (2024) · Journal of Physics Complexity · doi:10.1088/2632-072x/ad679f · Cited by 1
Malfeito-Ferreira, J., Kumano, M., & Kimura, M. (2025) · Studies in computational intelligence · doi:10.1007/978-3-031-82431-9_5 · Cited by 1
Master Stability for Traveling Waves on Networks
Ruschel, S., & Giraldo, A. (2025) · Physical Review Letters · doi:10.1103/6v64-j5fd · Cited by 1
HAS: Hypergraph Adaptive Sampling for Structural Characteristics Preservation
Zhang, J., Guo, J., Luo, Q., Xie, Z., Liu, G., & Cai, Z. (2025) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2025.3565008 · Cited by 1
Brusa, L., & Pennoni, F. (2025) · Behaviormetrics · doi:10.1007/978-981-96-7109-0_1 · Cited by 1
Knowledge attention via radial basis functions for temporal knowledge graphs completion
Wang, E., Liu, J., Liu, X., Huang, B., & Huang, X. Q. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.114336 · Cited by 1
Gao, C., Jiang, H., & Guo, X. (2025) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2025.111732 · Cited by 1
Time series analysis of coupled slow–fast neuron models: From Hurst exponent to Granger causality
Ghosh, I., Fatoyinbo, H. O., & Muni, S. S. (2025) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/5.0291493 · Cited by 1
Hard Negative Sampling in Hyperedge Prediction
Deng, Z., Zhou, T., & Bi, Y. (2025) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2025.3634750 · Cited by 1
Gao, C., Jiang, H., & Li, M. (2026) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2026.112372 · Cited by 1
Ding, W., & Itoh, E. (2026) · Journal of Transport Geography · doi:10.1016/j.jtrangeo.2026.104635 · Cited by 1
Wang, R., Carletti, T., & Bianconi, G. (2026) · Journal of Statistical Mechanics Theory and Experiment · doi:10.1088/1742-5468/ae4bb7 · Cited by 1
Ethereum phishing scam detection via multi-view contrastive learning on higher-Order networks
Liu, S., Yang, J., Yu, X., Chen, Z., Qi, Z., Long, H., & Liu, B. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.132189 · Cited by 1
Reconstruction of 3-simplex based on Bayesian inference
Sun, X., Fang, A., Sun, X., & Liu, X. (2026) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2026.131577 · Cited by 1
Conservative dynamics in phase oscillator networks
PIKOVSKY, A. S. (2026) · Journal of Physics Complexity · doi:10.1088/2632-072x/ae669b · Cited by 1
He, J., Liu, Y., Xiao, M., & Zhu, P. (2026) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2026.3691134 · Cited by 1
Higher-order homophily analysis for hypergraphs with time-varying node categories
Kumano, M., Nishimura, K., & Kimura, M. (2026) · Applied Network Science · doi:10.1007/s41109-026-00801-7 · Cited by 1
On the efficiency of pairwise Hamiltonian control to desynchronize the higher-order Kuramoto model
Moriamé, M., Muolo, R., Carletti, T., & Lucas, M. (2026) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/5.0329310 · Cited by 1
Fibration symmetries and cluster synchronization in multi-body systems
Bertè, M., & Gili, T. (2026) · Scientific Reports · doi:10.1038/s41598-026-61679-0 · Cited by 1
Higher-order trade-offs in hypergraph community detection
Li, J., Schaub, M. T., & Peel, L. (2026) · Science Advances · doi:10.1126/sciadv.aef2184 · Cited by 1
Self-citationYang, D., & Zhang, W. (2026) · Mathematics · doi:10.3390/math14162862 · Cited by 1
Maslennikov, O. V., & Nekorkin, V. I. (2026) · The European Physical Journal Special Topics · doi:10.1140/epjs/s11734-026-02595-2 · Cited by 1
Models and Methods for Sparse (Hyper)Network Science in Business, Industry, and Government
Aksoy, S. G., Hagberg, A. A., Joslyn, C., Kay, B., Purvine, E., & Young, S. J. (2022) · Notices of the American Mathematical Society · doi:10.1090/noti2424 · Cited by 1
Higher-order Link Prediction Based on Message Passing Simplicial Networks
Fu, J., & Chen, C. (2024) · International Conference on Intelligent Control and Information Processing · doi:10.1109/icicip60808.2024.10477790 · Cited by 1
Identification of missing higher-order interactions in complex networks
Zhang, C., Suxun, W., Yu, W., Zhao, P., Chen, Y., Gu, J., Ren, Z., & Liu, J. (2024) · Journal of Complex Networks · doi:10.1093/comnet/cnae031 · Cited by 1
EPCDescriptor: A Multi-Attribute Visual Network Modeling of Housing Energy Performance
Shakeel, H. M., Iram, S., Farid, H. M. A., Hill, R. C., & Rehman, H. U. (2025) · Buildings · doi:10.3390/buildings15162929 · Cited by 1
Multiple timescale dynamics of network adaptation with constraints
Martens, E. A., & Bick, C. (2025) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/5.0289706 · Cited by 1
Self-citationAnalysis of Turing Instability of the Fitzhugh-Nagumo Model in Diffusive Network
Gao, S. (2024) · Journal of Advances in Mathematics and Computer Science · doi:10.9734/jamcs/2024/v39i51890 · Cited by 1
Eigenvalues and spectral gap in sparse random simplicial complexes
Leibzirer, S., & Rosenthal, R. (2025) · Annales de l Institut Henri Poincaré Probabilités et Statistiques · doi:10.1214/24-aihp1462 · Cited by 1
Chasing “strange animals”: Network analysis tools for the economic study of supra-dyadic structures
Lucena‐Piquero, D., Ugolini, S., & Vicente, J. (2025) · Journal of Evolutionary Economics · doi:10.1007/s00191-025-00903-y · Cited by 1
Papo, D., & Buldú, J. M. (2025) · Physics of Life Reviews · doi:10.1016/j.plrev.2025.08.001 · Cited by 1
Metadata
- Authors
- Bick, C., Gross, E., Harrington, H. A., & Schaub, M. T.
- Affiliations
- Vrije Universiteit Amsterdam (NL)
- University of Hawaiʻi at Mānoa (US)
- Centre for Human Genetics (GB)
- Venue
- SIAM Review
- Published
- 2023-08
- Volume
- 65
- Issue
- 3
- Pages
- 686-731
- ISSN
- 0036-1445 (ISSN-L)
- 1095-7200
- OpenAlex
- W3158396846
- Cited by
- 420
- References
- 316
- Citations by year
- 2026: 133 · 2025: 141 · 2024: 82 · 2023: 34
- FWCI
- 38.87
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- SIAM Review (publishedVersion, cc-by)
- RWTH Publications (RWTH Aachen) (publishedVersion)
- VU Research Portal (publishedVersion, other-oa)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- H2020 Marie Skłodowska-Curie Actions (702410)
- Technische Universitat Munchen
- Ministerium für Innovation, Wissenschaft und Forschung des Landes Nordrhein-Westfalen
- National Science Foundation (DMS-1945584)
- Royal Society (RGFnEAn201074, UF150238)
- Engineering and Physical Sciences Research Council (EP/T013613/1, EP/R018472/1, EP/R005125/1, EP/T001968/1)
- Topics
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Graph Theory and Algorithms
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Topological and Geometric Data Analysis
- Keywords
- higher-order networks, hypergraphs, pairwise relationships, graph-based modeling, complex systems, network science
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review