Package index • hypernets

Package

hypernets hypernets-package: hypernets: Higher-Order Network Analysis

Reading results

One reader for every object.

hg_get(): Read a table from any hypernets object
hg_compare_communities() print(<hypernets_community_comparison>) hg_get(<hypernets_community_comparison>) plot(<hypernets_community_comparison>): Compare community structure across representations
print(<hg_communities>) hg_get(<hg_communities>) plot(<hg_communities>): Tables of a hypergraph community ensemble
hg_get(<net_hg>): Tables of a hypergraph
hg_get(<net_hg_cluster>): Assignment table of a hypergraph clustering fit
hg_get(<net_hg_measures>): Tables of a hypergraph measures fit
hg_get(<net_hg_transduction>): Tables of a hypergraph transduction fit
hg_get(<net_hon>): Tables of a higher-order network
hg_get(<net_hon_boot>): Inference table of a HON bootstrap
hg_get(<net_hon_communities>): Tidy tables of a memory-network community result
hg_get(<net_hon_compare>): Edge table of a HON comparison
print(<net_hon_group>) hg_get(<net_hon_group>) print(<net_hon_boot_group>) hg_get(<net_hon_boot_group>): Tables of a group model of memory networks
hg_get(<net_honem>): Tables of a higher-order network embedding
hg_get(<hypernets_hypa>) hg_get(<net_hypa>): Tables of a path-anomaly (HYPA) fit
hg_get(<net_markov_order>): Tables of a Markov order test
hg_get(<net_markov_order_group>): Tables of a grouped Markov order test
hg_get(<net_markov_stability>): Tables of a Markov stability analysis
hg_get(<net_mogen>): Tables of a multi-order generative model
hg_get(<net_path_dependence>): Per-context table of a path-dependence analysis
hg_get(<net_temporal_hypergraph>): Tidy tables of a temporal hypergraph
hg_get(<persistence_landscape>): Table of a persistence landscape
hg_get(<persistent_homology>): Tables of a persistent homology result
hg_get(<q_analysis>): Tables of a q-analysis
hg_get(<simplicial_complex>): Tables of a simplicial complex
hg_get(<text_hypergraph>): Tidy tables of a text hypergraph
hg_mmsbm() hg_get(<net_hg_mmsbm>) print(<net_hg_mmsbm>) plot(<net_hg_mmsbm>): Mixed-membership communities of a hypergraph (Hy-MMSBM)
hg_motifs() hg_get(<hypernets_motifs>) plot(<hypernets_motifs>): Y, T and O motif census with a hypergraph configuration null
hg_topics() hg_get(<net_hg_topics>) print(<net_hg_topics>) plot(<net_hg_topics>): Mixed-membership topic model of a hypergraph
hg_get(<hypernets_result>) print(<hypernets_result>) plot(<hypernets_result>): Print a result of an imported estimator
hg_get(<hypernets_summary>) print(<hypernets_summary>) summary(<hypernets_result>) summary(<hypernets_hypa>) summary(<hypernets_community_comparison>) summary(<hg_communities>) summary(<net_hg_mmsbm>) summary(<hypernets_motifs>) summary(<net_hon_communities>) summary(<net_hon_boot_group>) summary(<net_hon_group>) summary(<net_hon_boot>) summary(<net_hon_compare>) summary(<hypernets_simplicial>) summary(<net_hg_topics>): Summaries of results
simplicial() plot(<hypernets_simplicial>) hg_get(<hypernets_simplicial>) print(<hypernets_simplicial>): Simplicial complex from sequences, a network or a memory network

Memory networks

A node is a state plus the memory of how it was reached.

sequence-input: Sequence input shared by the sequence verbs
hon(): Higher-order network (HON) from sequences
honem(): Embedding of a higher-order network (HONEM)
mogen(): Multi-order generative model (MOGen) of sequences
hypa(): Hypergeometric anomalies of paths or of co-occurring node pairs
hypa(<default>) print(<hypernets_hypa>): Path anomalies (HYPA) in sequences
hg_centrality(): Centralities of a hypergraph or a memory network
hg_centrality(<net_hon>): Higher-order centralities with first-order projection
hg_communities(): Communities of a hypergraph or a memory network
hg_communities(<net_hon>): Memory-network communities with the map equation
hg_bootstrap(): Bootstrap inference for higher-order network rules
hg_compare(): Permutation comparison of the rules of two groups
markov_order(): Test the Markov order of sequences
hg_markov_stability(): Markov stability of the states of a transition network
memory(): Per-context path dependence

Simplicial complexes

A set of mutually related nodes together with every subset.

simplicial() plot(<hypernets_simplicial>) hg_get(<hypernets_simplicial>) print(<hypernets_simplicial>): Simplicial complex from sequences, a network or a memory network
hg_betti(): Betti numbers of a simplicial complex
hg_euler(): Euler characteristic of a simplicial complex
hg_degree(): Simplicial degree of every node
hg_qanalysis(): Q-analysis of a simplicial complex
hg_homology(): Persistent homology of a weighted network or a distance matrix
hg_landscape(): Persistence landscape of a persistence diagram
hg_bottleneck(): Bottleneck distance between persistence diagrams
hg_wasserstein(): Wasserstein Distance Between Persistence Diagrams

Hypergraphs

An arbitrary set of nodes bound as a unit.

Constructors

network_hypergraph() print(<net_hg>) summary(<net_hg>): Higher-order hypergraph from a network's clique structure
window_hypergraph(): Windowed Sequence Hyperedges
group_hypergraph(): Hypergraph from co-occurrence data or an edge list
knn_hypergraph(): Build a k-nearest-neighbor hypergraph from embeddings
dual_hypergraph(): Dual of a hypergraph
temporal_hypergraph(): Temporal hypergraph from an edge list or co-occurrence data
hg_snapshot(): Extract one snapshot from a temporal hypergraph
hg_snapshots(): Extract a sequence of temporal-hypergraph snapshots
hg_subset(): Sub-hypergraph by hyperedges, nodes or hyperedge attributes
hg_sample_gnp(): Sample Bernoulli-Incidence Random Hypergraphs
hg_sample_sbm(): Sample Stochastic-Block-Model Hypergraphs
hg_sample_uniform() hg_sample_regular(): Sample Uniform or Regular Random Hypergraphs
hg_write_hif() hg_read_hif(): Read and write the Hypergraph Interchange Format (HIF)

Measures and centralities

print(<net_hg_measures>) hg_measures(): Structural measures for a hypergraph
hg_centrality(<net_hg>): Hypergraph eigenvector centralities
hg_pagerank(): Hypergraph PageRank with edge-dependent vertex weights
hg_transitivity(): Local clustering coefficients of a hypergraph
hg_assortativity(): Degree assortativity of a hypergraph
hg_degree_correlation(): Correlation of hyperdegrees across interaction orders
hg_edges(): Hyperedge-level measures, as a tidy table
hg_edge_centrality(): s-betweenness and s-closeness centrality of hyperedges
hg_growth() plot(<hypernets_series>): Growth of a temporal hypergraph over time
hg_representations(): Graph and hypergraph representations of the same data
hg_motifs() hg_get(<hypernets_motifs>) plot(<hypernets_motifs>): Y, T and O motif census with a hypergraph configuration null

Spectral methods

hg_laplacian(): Normalized hypergraph Laplacian
hg_cluster(): Spectral or symmetric-NMF clustering of hypergraph vertices
hg_joint_cluster(): Joint NMF clustering with an auxiliary vertex relation
hg_embed(): Low-Dimensional Hypergraph Embedding
hg_classify(): Transductive label spreading on a hypergraph

Projections

hg_clique_expansion(): Clique expansion of a hypergraph
hg_project(): Project a hypergraph onto a weighted graph
hg_line_graph(): The s-line graph of a hypergraph

Communities

hg_communities(<net_hg>): Stable Infomap communities of a hypergraph projection
hg_community_quality(): Quality of a projected-hypergraph partition
hg_modularity(): Hypergraph modularity of a partition
hg_compare_communities() print(<hypernets_community_comparison>) hg_get(<hypernets_community_comparison>) plot(<hypernets_community_comparison>): Compare community structure across representations

Null models and neural networks

hg_null_test(): Degree-preserving null-model test of hypergraph structure
hypa(<net_hg>): Hypergeometric anomaly detection for co-occurring node pairs
hg_neural(): Hypergraph neural network classifier (HGNN)
hg_hypergat() text_hypergat(): HyperGAT document classifier
heterogeneous_hgat(): Heterogeneous graph attention classifier (HGAT)
hg_hypergcn(): HyperGCN node classifier
hg_hnhn(): HNHN node classifier
hg_allset(): AllSet hypergraph node classifier

Text hypergraphs

A corpus as a weighted document-word hypergraph, analysed with tidy verbs.

clean_text(): Clean a corpus before building a text hypergraph
text_hypergraph(): Build a weighted hypergraph from a text corpus
stop_words_en(): English function-word stop list
print(<net_hg_measures>) hg_measures(): Structural measures for a hypergraph
hg_centrality(): Centralities of a hypergraph or a memory network
hg_cluster(): Spectral or symmetric-NMF clustering of hypergraph vertices
hg_keywords() print(<hypernets_keywords>) plot(<hypernets_keywords>): Characteristic words (keywords) per cluster
hg_sequences(): Per-actor state sequences from a clustered corpus
hg_relations(): Relations between topics: a weighted network of shared vocabulary
hg_topic_sizes() plot(<hypernets_topic_sizes>): Topic sizes
hg_topics() hg_get(<net_hg_topics>) print(<net_hg_topics>) plot(<net_hg_topics>): Mixed-membership topic model of a hypergraph
hg_topic_search() plot(<hypernets_topic_search>): Choose the number of topics by coherence and exclusivity
hg_topic_quality() plot(<hypernets_topic_quality>): Topic coherence and exclusivity
hg_cocluster(): Co-clustering of nodes and hyperedges (documents and words)
hg_membership() plot(<hypernets_membership>): Soft topic membership from the spectral embedding
hg_mmsbm() hg_get(<net_hg_mmsbm>) print(<net_hg_mmsbm>) plot(<net_hg_mmsbm>): Mixed-membership communities of a hypergraph (Hy-MMSBM)
hg_stability(): Stability of a hypergraph clustering across resolutions
hg_agreement(): Agreement between two labelings of the same nodes
hg_seeds(): Seed labels from a clustering, for spreading or training
hg_classify(): Transductive label spreading on a hypergraph

Methods

hg_compare_communities() print(<hypernets_community_comparison>) hg_get(<hypernets_community_comparison>) plot(<hypernets_community_comparison>): Compare community structure across representations
print(<hg_communities>) hg_get(<hg_communities>) plot(<hg_communities>): Tables of a hypergraph community ensemble
print(<net_hon_group>) hg_get(<net_hon_group>) print(<net_hon_boot_group>) hg_get(<net_hon_boot_group>): Tables of a group model of memory networks
hg_keywords() print(<hypernets_keywords>) plot(<hypernets_keywords>): Characteristic words (keywords) per cluster
print(<net_hg_measures>) hg_measures(): Structural measures for a hypergraph
hg_mmsbm() hg_get(<net_hg_mmsbm>) print(<net_hg_mmsbm>) plot(<net_hg_mmsbm>): Mixed-membership communities of a hypergraph (Hy-MMSBM)
hg_topics() hg_get(<net_hg_topics>) print(<net_hg_topics>) plot(<net_hg_topics>): Mixed-membership topic model of a hypergraph
hypa(<default>) print(<hypernets_hypa>): Path anomalies (HYPA) in sequences
network_hypergraph() print(<net_hg>) summary(<net_hg>): Higher-order hypergraph from a network's clique structure
hg_get(<hypernets_result>) print(<hypernets_result>) plot(<hypernets_result>): Print a result of an imported estimator
print(<net_hg_cluster>): Print method for net_hg_cluster
print(<net_hg_transduction>): Print method for net_hg_transduction
print(<net_hon_boot>): Print method for net_hon_boot
print(<net_hon_communities>): Print a memory-network community result
print(<net_hon_compare>): Print method for net_hon_compare
print(<text_hypergraph>): Print a text hypergraph
hg_get(<hypernets_summary>) print(<hypernets_summary>) summary(<hypernets_result>) summary(<hypernets_hypa>) summary(<hypernets_community_comparison>) summary(<hg_communities>) summary(<net_hg_mmsbm>) summary(<hypernets_motifs>) summary(<net_hon_communities>) summary(<net_hon_boot_group>) summary(<net_hon_group>) summary(<net_hon_boot>) summary(<net_hon_compare>) summary(<hypernets_simplicial>) summary(<net_hg_topics>): Summaries of results
simplicial() plot(<hypernets_simplicial>) hg_get(<hypernets_simplicial>) print(<hypernets_simplicial>): Simplicial complex from sequences, a network or a memory network
summary(<net_hg_cluster>): Summary method for net_hg_cluster
summary(<net_hg_transduction>): Summary method for net_hg_transduction
hg_growth() plot(<hypernets_series>): Growth of a temporal hypergraph over time
hg_membership() plot(<hypernets_membership>): Soft topic membership from the spectral embedding
hg_motifs() hg_get(<hypernets_motifs>) plot(<hypernets_motifs>): Y, T and O motif census with a hypergraph configuration null
hg_topic_quality() plot(<hypernets_topic_quality>): Topic coherence and exclusivity
hg_topic_search() plot(<hypernets_topic_search>): Choose the number of topics by coherence and exclusivity
hg_topic_sizes() plot(<hypernets_topic_sizes>): Topic sizes
plot(<net_hg>): Plot a hypergraph with hyperedges as pebbles
plot(<net_hg_cluster>): Plot method for net_hg_cluster
plot(<net_hg_transduction>): Plot method for net_hg_transduction
plot(<net_hon_boot>): Plot method for net_hon_boot
plot(<net_hon_communities>): Plot memory-network communities
plot(<net_hon_compare>): Plot method for net_hon_compare
plot(<net_temporal_hypergraph>): Plot a temporal-hypergraph snapshot through cograph

Data

human_long ai_long: Human-AI Vibe Coding Interaction Data (Long Format)
covid_abstracts: COVID-19 education research abstracts
covid_sample: A random sample of 1,000 COVID-19 education research abstracts
covid_embeddings: Sentence embeddings of the COVID-19 abstracts
icsid_tribunals: ICSID arbitration tribunals, one row per seat
gfcc_decisions: Decisions of the German Federal Constitutional Court
gfcc_citations: Citation blocks of the German Federal Constitutional Court
ring_sequences: Simulated sequences with planted memory modules
ring_communities: Planted communities of the ring sequences