Package: hypergraphs
Title: Higher-Order Network Analysis
Version: 0.7.5
Authors@R: c(
    person("Mohammed", "Saqr", email = "saqr@saqr.me", role = c("aut", "cre", "cph"),
           comment = c(ORCID = "0000-0001-5881-3109"))
    )
Description: 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)
        <doi: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) <doi:10.1126/sciadv.1600028>,
        higher-order network embedding Saebi et al. (2020)
        <doi:10.1089/big.2019.0169>, hypergeometric path anomaly
        detection LaRock et al. (2020)
        <doi:10.1137/1.9781611976236.52>, the multi-order generative
        model Scholtes (2017) <doi:10.1145/3097983.3098145>,
        higher-order centralities Scholtes, Wider, and Garas (2016)
        <doi: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) <doi:10.1137/18M1203031>; PageRank
        with edge-dependent vertex weights Chitra and Raphael (2019)
        <doi:10.48550/arXiv.1905.08287>; the weighted normalized
        hypergraph Laplacian of Zhou, Huang, and Scholkopf (2006)
        <doi:10.7551/mitpress/7503.003.0205> with spectral, symmetric
        non-negative matrix factorization (NMF), and joint-NMF
        clustering Hayashi et al. (2020) <doi: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.
License: MIT + file LICENSE
URL: https://github.com/mohsaqr/hypergraphs,
        https://pak.dynasite.org/hypergraphs/
BugReports: https://github.com/mohsaqr/hypergraphs/issues
Language: en-US
Encoding: UTF-8
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.3.3
Depends: R (>= 4.4)
Imports: cograph (>= 2.7.2), ggplot2, grDevices, grid, Matrix, methods,
        Nestimate (>= 0.9.26), parallel, RSpectra, stats, tools, utils
Suggests: gridExtra, igraph, knitr, png, rmarkdown, sbert, testthat (>=
        3.0.0), torch
Additional_repositories: https://mohsaqr.r-universe.dev
VignetteBuilder: knitr
Config/testthat/edition: 3
LazyData: true
Repository: https://mohsaqr.r-universe.dev
Date/Publication: 2026-10-16 15:00:42 UTC
RemoteUrl: https://github.com/mohsaqr/hypergraphs
RemoteRef: HEAD
RemoteSha: b30bc355100f332ae94fa61ff8976454555962ea
NeedsCompilation: no
Packaged: 2026-10-16 17:42:36 UTC; root
Author: Mohammed Saqr [aut, cre, cph] (ORCID:
    <https://orcid.org/0000-0001-5881-3109>)
Maintainer: Mohammed Saqr <saqr@saqr.me>