net_by_degree(), net_by_indegree(), net_by_outdegree(), net_by_betweenness(), net_by_closeness(), and net_by_eigenvector() now return a single network-level score for two-mode networks (via Freeman's general centralization index over the mode-normalized node scores), consistent with returning a scalar network_measure for all networksmode_by_*() family (mode_by_degree(), mode_by_indegree(), mode_by_outdegree(), mode_by_betweenness(), mode_by_closeness(), mode_by_eigenvector()) that returns the per-mode centralization scores for two-mode networks, following Borgatti and Everett (1997); these error on one-mode networksnet_by_betweenness() to respect its normalized argument for one-mode networks, which was previously ignored because igraph::centr_betw() always applied its default normalizationnet_by_closeness() and mode_by_closeness() to pass their direction argument through to the underlying node scores, so direction = "in"/"all" is now effective for two-mode networksnetrics1) with a new interactive style, and added an article version to the websitenetrics2) with a new versionnetrics3) with a new interactive stylenetrics4) with a new interactive stylenode_by_homophily() to work when attribute is provided as a vector (e.g., a membership vector)node_by_homophily() to avoid calling as_igraph() multiple timesnet_x_hazard() to use diff_model$t for naming the returned data frame columns, rather than the deprecated diff_model$time{manynet}node_in_partition()param_attr, param_data,
param_dir, param_memb, param_motf, param_norm, param_select) and
net/node/tie-level templates (net_measure, net_motif, node_mark,
node_measure, node_member, node_motif, tie_mark, tie_measure) for
consistent function documentation.node_adoption_time() to node_by_adopt_time()node_thresholds() to node_by_adopt_threshold()node_exposure() to node_by_adopt_exposure() node_recovery() to node_by_adopt_recovery() node_in_community() documentation from the hierarchical
and non-hierarchical community-detection algorithms.net_by_change() to net_x_change() and related functions to
reflect their motif (subgraph-counting) nature.method_k().model_k() to method_k() and related cluster-selection utilities
renamed for clarity.{netrics} 0.1.0 is the first formal release of the package as a standalone
analytic engine for the stocnet ecosystem.
The analytic functions — marks, measures, motifs, and memberships — have been
extracted from {manynet} and {migraph} into this dedicated package, with
consistent naming conventions and a range of bug fixes.
All functions now follow a consistent verb–object–qualifier naming scheme:
node_is_*(), tie_is_*()): logical vectors identifying which nodes
or ties hold a particular structural property.*_by_*()): numeric vectors at the network (net_by_*()),
node (node_by_*()), or tie (tie_by_*()) level.*_x_*()): tabular counts of nodes' or networks' participation
in structural sub-patterns.*_in_*()): categorical vectors assigning nodes to groups
(components, communities, equivalence classes, etc.).Functions previously named with other prefixes (e.g. node_centrality_*,
net_cohesion_*, node_equivalency_*) have been renamed to follow the
*_by_*() / *_x_*() / *_in_*() convention.
tie_by_cohesion() now correctly returns a tie_measure class object.
{manynet} / {migraph}The following groups of functions have been moved into {netrics}:
node_is_core(), node_is_cutpoint(), node_is_exposed(),
node_is_fold(), node_is_independent(), node_is_infected(),
node_is_isolate(), node_is_latent(), node_is_max(),
node_is_mean(), node_is_mentor(), node_is_min(),
node_is_neighbor(), node_is_pendant(), node_is_random(),
node_is_recovered(), node_is_universal()tie_is_bridge(), tie_is_cyclical(), tie_is_feedback(),
tie_is_imbalanced(), tie_is_loop(), tie_is_max(),
tie_is_min(), tie_is_multiple(), tie_is_path(),
tie_is_random(), tie_is_reciprocated(), tie_is_simmelian(),
tie_is_transitive(), tie_is_triangular(), tie_is_triplet()net_by_adhesion(), net_by_assortativity(),
net_by_balance(), net_by_betweenness(), net_by_change(),
net_by_closeness(), net_by_cohesion(), net_by_components(),
net_by_congruency(), net_by_connectedness(), net_by_core(),
net_by_correlation(), net_by_degree(), net_by_density(),
net_by_diameter(), net_by_diversity(), net_by_efficiency(),
net_by_eigenvector(), net_by_equivalency(), net_by_factions(),
net_by_harmonic(), net_by_heterophily(), net_by_hierarchy(),
net_by_homophily(), net_by_immunity(), net_by_indegree(),
net_by_independence(), net_by_infection_complete(),
net_by_infection_peak(), net_by_infection_total(),
net_by_length(), net_by_modularity(), net_by_outdegree(),
net_by_reach(), net_by_reciprocity(), net_by_recovery(),
net_by_reproduction(), net_by_richclub(), net_by_richness(),
net_by_scalefree(), net_by_smallworld(), net_by_spatial(),
net_by_stability(), net_by_strength(), net_by_toughness(),
net_by_transitivity(), net_by_transmissibility(),
net_by_upperbound(), net_by_waves()node_by_adoption_time(), node_by_alpha(),
node_by_authority(), node_by_betweenness(), node_by_bridges(),
node_by_brokering_activity(), node_by_brokering_exclusivity(),
node_by_closeness(), node_by_constraint(), node_by_coreness(),
node_by_deg(), node_by_degree(), node_by_distance(),
node_by_diversity(), node_by_eccentricity(),
node_by_efficiency(), node_by_effsize(), node_by_eigenvector(),
node_by_equivalency(), node_by_exposure(), node_by_flow(),
node_by_harmonic(), node_by_heterophily(), node_by_hierarchy(),
node_by_homophily(), node_by_hub(), node_by_indegree(),
node_by_induced(), node_by_information(), node_by_kcoreness(),
node_by_leverage(), node_by_multidegree(),
node_by_neighbours_degree(), node_by_outdegree(),
node_by_pagerank(), node_by_posneg(), node_by_power(),
node_by_randomwalk(), node_by_reach(), node_by_reciprocity(),
node_by_recovery(), node_by_redundancy(), node_by_richness(),
node_by_stress(), node_by_subgraph(), node_by_thresholds(),
node_by_transitivity(), node_by_vitality()tie_by_betweenness(), tie_by_closeness(),
tie_by_cohesion(), tie_by_degree(), tie_by_eigenvector()net_x_brokerage(), net_x_dyad(), net_x_hazard(),
net_x_mixed(), net_x_tetrad(), net_x_triad()node_x_brokerage(), node_x_dyad(), node_x_exposure(),
node_x_path(), node_x_tetrad(), node_x_tie(), node_x_triad()node_in_adopter(), node_in_automorphic(),
node_in_betweenness(), node_in_brokering(),
node_in_community(), node_in_component(), node_in_core(),
node_in_eigen(), node_in_equivalence(), node_in_fluid(),
node_in_greedy(), node_in_infomap(), node_in_leiden(),
node_in_louvain(), node_in_optimal(), node_in_partition(),
node_in_regular(), node_in_roulette(), node_in_spinglass(),
node_in_strong(), node_in_structural(), node_in_walktrap(),
node_in_weak()node_is_isolate() and node_is_pendant() now work correctly with signed networks.tie_is_random() now correctly returns a tie_mark class object (previously returned a node mark).node_by_authority() and node_by_hub() updated to use current {igraph} API.node_by_brokering_activity() and node_by_brokering_exclusivity() now handle unlabelled networks correctly.node_by_homophily() no longer resolves the attribute to a vector prematurely.node_by_pagerank() updated to correctly extract the vector output from {igraph}.node_by_power() reverts to a lower exponent (closer to degree centrality) when there is no degree variation.node_by_randomwalk() now works with two-mode networks.net_by_degree(), net_by_harmonic(), and net_by_reach() now consistently include the function call in the returned object.net_by_richclub() returns 0 (rather than erroring) when all nodes have equivalent degree.net_by_smallworld() and node_by_bridges() now use internal {netrics} functions rather than {manynet} equivalents.net_by_waves() returns 1 for cross-sectional networks and correctly returns a network measure class.net_x_hierarchy() correctly classified as a motif function.node_in_community() now delegates to {netrics} membership functions internally.tie_by_cohesion() now correctly returns a tie_measure class object.Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.