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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.
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“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
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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
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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.
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