Computes comprehensive graph-level statistics for netify objects, including density, reciprocity, centralization measures, and custom metrics. Handles cross-sectional and longitudinal networks, as well as multilayer structures.
Usage
# S3 method for class 'netify'
summary(object, ...)Value
A data frame with one row per network/time period containing:
Basic network properties:
netNetwork/time identifier
layerLayer name (for multilayer networks)
num_actorsNumber of actors (or
num_row_actorsandnum_col_actorsfor bipartite networks)densityProportion of possible ties that exist
num_edgesTotal number of edges (unweighted count)
prop_edges_missingProportion of potential edges that are NA
For weighted networks only:
mean_edge_weightAverage weight of existing edges
sd_edge_weightStandard deviation of edge weights
median_edge_weightMedian edge weight
min_edge_weight,max_edge_weightRange of edge weights
Structural measures:
competition(orcompetition_row/competition_col)Herfindahl-Hirschman Index measuring concentration of ties. Calculated as \(\sum_{i=1}^{n} (s_i)^2\) where \(s_i\) is actor i's share of total ties. Ranges from 1/n (equal distribution) to 1 (one actor has all ties).
sd_of_actor_means(orsd_of_row_means/sd_of_col_means)Standard deviation of actors' average tie strengths, measuring heterogeneity in actor activity levels
transitivityGlobal clustering coefficient (probability that two neighbors of a node are connected)
For directed networks only:
covar_of_row_col_meansCovariance between actors' sending and receiving patterns
reciprocityCorrelation between adjacency matrix and its transpose, measuring tendency for mutual ties
Details
The function automatically adapts calculations based on network properties:
Bipartite networks: Reports row and column actors separately
Directed networks: Calculates separate statistics for in/out ties
Weighted networks: Includes weight-based statistics
Multilayer networks: Processes each layer independently
Longitudinal networks: Calculates statistics for each time period
Competition Index Interpretation:
The competition measure (HHI) captures how concentrated network ties are among actors. Low values indicate distributed activity across many actors (competitive), while high values indicate concentration among few actors (monopolistic). This is particularly useful for analyzing power dynamics or resource distribution in networks.
Custom Statistics:
Add custom graph-level metrics using the other_stats parameter:
# Example: Community detection
modularity_stat <- function(mat) {
g <- netify_to_igraph(mat)
comm <- igraph::cluster_walktrap(g)
c(modularity = igraph::modularity(comm),
n_communities = length(unique(comm$membership)))
}
summary(net, other_stats = list(community = modularity_stat))Note
For large longitudinal or multilayer networks, computation can be intensive.
Consider using subset_netify to analyze specific time periods
or layers.
Missing edges (NA values) are excluded from density calculations but tracked
in the prop_edges_missing statistic.
References
Dorff, C., Gallop, M., & Minhas, S. (2023). "Networks of violence: Predicting conflict in Nigeria." Journal of Politics, 85(1).
Examples
# Load example data
data(icews)
# Basic usage
net <- netify(
icews,
actor1 = 'i', actor2 = 'j', time = 'year',
symmetric = FALSE,
weight = 'verbCoop'
)
# get summary
summary(net)
#> net num_actors density num_edges prop_edges_missing mean_edge_weight
#> 1 2002 152 0.3762119 8692 0 19.59123
#> 2 2003 152 0.3846520 8887 0 19.36358
#> 3 2004 152 0.4117902 9514 0 19.84986
#> 4 2005 152 0.4045187 9346 0 19.79488
#> 5 2006 152 0.4081111 9429 0 21.09136
#> 6 2007 152 0.4215287 9739 0 21.89753
#> 7 2008 152 0.4323061 9988 0 21.03381
#> 8 2009 152 0.4265062 9854 0 20.33139
#> 9 2010 152 0.4317867 9976 0 18.13393
#> 10 2011 152 0.4201870 9708 0 15.79217
#> 11 2012 152 0.4259436 9841 0 15.99564
#> 12 2013 152 0.4289733 9911 0 17.21301
#> 13 2014 152 0.4230436 9774 0 18.39291
#> sd_edge_weight median_edge_weight min_edge_weight max_edge_weight
#> 1 143.8213 0 0 6003
#> 2 144.5982 0 0 5937
#> 3 137.7292 0 0 5141
#> 4 148.6667 0 0 6561
#> 5 160.0782 0 0 7579
#> 6 162.4166 0 0 7125
#> 7 148.1206 0 0 6091
#> 8 156.6702 0 0 6889
#> 9 132.3670 0 0 4937
#> 10 113.5584 0 0 5581
#> 11 107.5946 0 0 4225
#> 12 130.4925 0 0 6320
#> 13 136.5412 0 0 6327
#> competition_row competition_col sd_of_row_means sd_of_col_means
#> 1 0.04093116 0.03813621 44.91530 43.04935
#> 2 0.04199822 0.03930188 45.07759 43.32783
#> 3 0.03481011 0.03262993 41.25503 39.63005
#> 4 0.04067046 0.03624472 45.20964 42.17312
#> 5 0.04151400 0.03697499 48.76301 45.48502
#> 6 0.03887627 0.03514325 48.67808 45.77854
#> 7 0.03785375 0.03421258 46.01192 43.25059
#> 8 0.04500583 0.03915054 49.29918 45.38806
#> 9 0.04071163 0.03493156 41.44120 37.76971
#> 10 0.03964994 0.03405077 35.52390 32.37731
#> 11 0.03587638 0.03146233 33.86660 31.21128
#> 12 0.04058869 0.03642385 39.26572 36.78299
#> 13 0.04058415 0.03521224 41.95447 38.49827
#> covar_of_row_col_means reciprocity transitivity
#> 1 0.9946888 0.9778217 0.6058952
#> 2 0.9872959 0.9632488 0.6072045
#> 3 0.9923809 0.9769563 0.6215978
#> 4 0.9932157 0.9804325 0.6215075
#> 5 0.9909337 0.9771928 0.6277829
#> 6 0.9940837 0.9783703 0.6330626
#> 7 0.9916659 0.9694667 0.6401296
#> 8 0.9946612 0.9800665 0.6318319
#> 9 0.9934913 0.9823385 0.6386063
#> 10 0.9903188 0.9763723 0.6220630
#> 11 0.9877987 0.9702754 0.6337725
#> 12 0.9939394 0.9782226 0.6314429
#> 13 0.9904402 0.9730745 0.6297063
if (FALSE) { # \dontrun{
# Add custom statistics - community detection
comm_stats <- function(mat) {
g <- netify_to_igraph(mat)
comm <- igraph::cluster_spinglass(g)
c(n_communities = length(comm$csize),
modularity = comm$modularity)
}
# Apply to subset for efficiency
sub_net <- subset_netify(net, time = as.character(2013:2014))
summary(sub_net, other_stats = list(community = comm_stats))
} # }