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# CITATION.cff file created with {cffr} R package
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cff-version: 1.2.0
message: 'To cite package "hypergraphs" in publications use:'
type: software
license: MIT
title: 'hypergraphs: Higher-Order Network Analysis'
version: 0.7.5
doi: 10.32614/CRAN.package.hypergraphs
abstract: 'Build and analyze higher-order networks, in which a relation can involve
  more than two nodes or can depend on more than the current node. Covers the three
  structure families of the higher-order network literature Battiston et al. (2020)
  <https://doi.org/10.1016/j.physrep.2020.05.004>, plus a text family that reads a
  corpus as a hypergraph. Every object is read through one accessor, and every sequence-taking
  verb reads long, wide, list or model input. Memory networks from categorical sequences:
  higher-order networks with rule extraction Xu, Wickramarathne, and Chawla (2016)
  <https://doi.org/10.1126/sciadv.1600028>, higher-order network embedding Saebi et
  al. (2020) <https://doi.org/10.1089/big.2019.0169>, hypergeometric path anomaly
  detection LaRock et al. (2020) <https://doi.org/10.1137/1.9781611976236.52>, the
  multi-order generative model Scholtes (2017) <https://doi.org/10.1145/3097983.3098145>,
  higher-order centralities Scholtes, Wider, and Garas (2016) <https://doi.org/10.1140/epjb/e2016-60663-0>,
  a permutation-based Markov order test, a path-dependence diagnostic, and bootstrap
  and permutation inference for extracted rules. Simplicial complexes: clique, Vietoris-Rips
  and pathway complexes, Betti numbers, persistent homology, persistence landscapes,
  bottleneck and Wasserstein distances, and q-analysis. Hypergraphs: construction
  from network cliques, sliding windows over sequences, bipartite group membership,
  or embedding nearest neighbours; structural measures; tensor eigenvector centralities
  Benson (2019) <https://doi.org/10.1137/18M1203031>; PageRank with edge-dependent
  vertex weights Chitra and Raphael (2019) <https://doi.org/10.48550/arXiv.1905.08287>;
  the weighted normalized hypergraph Laplacian of Zhou, Huang, and Scholkopf (2006)
  <https://doi.org/10.7551/mitpress/7503.003.0205> with spectral, symmetric non-negative
  matrix factorization (NMF), and joint-NMF clustering Hayashi et al. (2020) <https://doi.org/10.1145/3340531.3412034>
  and transductive label spreading; projections to weighted graphs and s-line graphs;
  Bernoulli-incidence, stochastic-block, uniform and regular random generators; temporal
  snapshots; hyperedge betweenness and closeness; Infomap ensembles with adjusted-
  mutual-information medoid selection; Y/T/O motif profiles and null models by degree-preserving
  swaps and the configuration model; and hypergraph neural networks in native ''torch'':
  the hypergraph neural network (HGNN), hypergraph attention networks (HyperGAT and
  its heterogeneous variant), the hypergraph convolutional network (HyperGCN), hypergraph
  networks with hyperedge neurons (HNHN), AllDeepSets and AllSetTransformer. Text
  hypergraphs: a corpus becomes a weighted document-word hypergraph in either orientation
  (bag of words with smoothed term frequency-inverse document frequency (tf-idf),
  token windows, or embedding nearest neighbours), analysed with tidy verbs for clustering,
  keyword extraction, few-label classification, and null testing.'
authors:
- family-names: Saqr
  given-names: Mohammed
  email: saqr@saqr.me
  orcid: https://orcid.org/0000-0001-5881-3109
repository: https://mohsaqr.r-universe.dev
repository-code: https://github.com/mohsaqr/hypergraphs
commit: 9ec1d27f7ab7ced4ef4e15664ecb46a23450eac3
url: https://pak.dynasite.org/hypergraphs/
date-released: '2026-10-12'
contact:
- family-names: Saqr
  given-names: Mohammed
  email: saqr@saqr.me
  orcid: https://orcid.org/0000-0001-5881-3109