{roxytest}m argument into p for generate_random(), p can now be passed an integer to indicate the number of ties the network should haveto_edges() to be ~26 times faster on averageto_edges()to_subgraph() now instead of dplyr::filter() or strain()times argument instead of maxiter"measure" class "node_measure" and added "graph_measure" class with print methodnormalized is now the second argumentdirected and weights arguments have been removed and are now imputed, if this is undesired please use to_*() firstnode_degree() now calculates strength centrality if network is weightednode_eigenvector() and graph_eigenvector() both work with two-mode networksedge_degree() and edge_eigenvector(), which both just apply the corresponding nodal measure to the edge graphedge_mutual() renamed to edge_reciprocal()graph_assortativity()mpn_elite_mexison_brandesis_multiplex.igraph() and is_multiplex.tbl_graph() now checks for multiple edge attributesstrain() as wrapper for {dplyr}’s filter(), renamed to avoid conflicts with {stats}ison_algebra now unlabelled{readxl} is now suggested, but required if importing from an Excel sheet{patchwork} replaces {gridExtra} to make for more concise multiplot visualisations{dplyr} also serves to export {magrittr}’s pipe{RColorBrewer} has been dropped and the Dark2 discrete set of colors is now internaldirected and direction arguments in some functions; whereas directed is always logical (TRUE/FALSE), direction expects a character string, e.g. “in”, “out”, or “undirected”generate_permutation() now has an additional logical argument, with_attr, that indicates whether any attributes from the original data should be passed to the permuted objectcreate_*() functions now accept existing objects as their first argument and will create networks with the same dimensionsread_pajek() now imports nodal attributes alongside the main edgesread_ucinet() now enjoys clearer documentationas_*() functions now retain weights where present; if you want an unweighted result, use is_unweighted() afterwards
as_edgelist.network() now better handles edge weightsas_matrix.igraph() now better handles edge signsis_twomode(), is_directed(), is_weighted(), is_labelled(), is_signed(), is_multiplex(), is_complex(), and is_graph()as_edgelist(), and to_unweighted(), and improved the data.frame method for as_matrix()to_named() and to_unsigned()to_edges() for creating adjacency matrices using a network’s edges as nodesproject_rows() and project_cols() functions to to_mode1() and to_mode2(), which is both more consistent with other functions naming conventions and more generic by avoiding the matrix-based row/column distinctionnode_mode(), which returns a vector of the mode assignments of the nodes in a networkedge_signs(), which returns a vector of the sign assignments of the edges in a networkautographr() works and how {ggraph} extends thisautographr() now incorporates ggidentify() functionality (closed #150){patchwork} is now used to assemble multiple plots togetherggatyear()measure class and directed most node_*() functions to create objects of this class
ggdistrib() and offers “hist” and “dens” methods for histograms and density plots respectivelyedge_betweenness() wraps {igraph}’s function of the same nameedge_closeness() measures the closeness centrality of nodes in an edge adjacencynode_cuts() identifies articulation points (nodes) in a networkedge_bridges() identifies edges that serve as bridges in a networkgraph_cohesion() measures how many nodes would need to be removed to increase the number of components (closed #192)graph_adhesion() measures how many edges would need to be removed to increase the number of componentsgraph_length() measures the average path lengthgraph_diameter() measures the longest path lengthnode_smallworld() and added graph_smallworld(), which works with both one- and two-mode networks (fixed #214)node_quad_census()network_reg()’s formula-based system
network_reg() can now handle binary and multiple categorical variables (factors and characters, closed #211);network_reg() can now manage interactions specified in the common syntax; var1 * var2 expands to var1 + var2 + var1:var2 (closed #163)dist() and sim() effects have been added (closed #207)network_reg() now employs logistic regression to estimate a binary outcome and linear regression to estimate a continuous outcome (closed #184)network_reg() now uses Dekker et al’s semi-partialling procedure by default for multivariate specifications (closed #206), defaulting to y-permutations in the case of a single predictor (closed #208)network_reg(), relying on {furrr} for potential parallelisation and {progressr} for progress reports (closed #185, #186)test_random() and test_permutation(), relying on {furrr} for potential parallelisation and {progressr} for progress reports; note that nSim argument now times (closed #199){broom} S3 methods for netlm and netlogit class objects (closed #183)
tidy() extracts coefficients and related valuesglance()extracts model-level values such as R^2netlm and netlogit class objects (closed #216), which plots the empirical distribution for each test statistic, indicates percentiles relating to common critical values, and superimposes the observed coefficientscug_test and qap_test class objects, which plots the empirical distribution, highlighting tails beyond some critical value (closed #213), and superimposing the observed coefficient and, possibly, 0{sna}
print.block_model() replaces print.blockmodel()plot.block_model() replaces plot.blockmodel()ison_southern_women instead of southern_womenison_brandes instead of brandesison_networkers instead of ison_eiesison_algebra instead of ison_m182ison_adolescents instead of ison_colemanmpn_elite_mex is extended with data from Pajek and with help from Frank Heberison_networkers becomes named with information from {tnet}mpn_* and ison_* datasets, including references/sourcescluster_structural_equivalence() for isolates
{patchwork} to suggested packages in DESCRIPTIONadd_ functions
mutate_edges()add_node_attributes()is_ methods: is_multiplex(), is_uniplex(), is_acyclic()edge_ functions to identify edges by properties: edge_mutual(), edge_multiple(), edge_loop()read_nodelist() and read_edgelist()as_network() method to convert correctly form an {igraph} to a {network} object.ggraphgrid() documentationread_edgelist() and read_nodelist() to readxl to avoid Java dependencywrite_edgelist() and write_nodelist() to avoid Java dependency
as_network(), as_igraph(), and is_directed()read_ and write_ functions and updated documentation
read_edgelist() for importing edgelists from Excel and csv filesread_pajek() for importing .net and .paj fileswrite_edgelist(), write_nodelist, write_pajek(), and write_ucinet() for exporting into various file formats (Excel, csv, Pajek, and UCINET)is_graph() to check if an object is a graph or notas_network() to retain attributesas_ and to_ functions
as_ functions to convert from dataframes instead of tibblesas_igraph() functionto_undirected() function to work with network objectsto_main_component() function so that it retains vertex attributes in network objectsedge_attribute() to grab a named edge attribute from a graph/networkto_unweighted() to prevent conversion of network object into igraph object when deleting weightssummarise_statistics()network_reg() exampleautographr()ison_eies dataset for use in practical 7 vignetteas_matrix() method for networks now works with two-mode and weighted networksas_igraph() method for matrices now checks for weights independently of coercionas_igraph() method for networks now works with two-mode and weighted networksas_network() method for matrices now works with two-mode and weighted networksas_network() method for edgelists, igraph, and tidygraphs now works with weighted networksto_unnamed() method for edge liststo_simplex() method for matricesto_main_component() method for networksto_multilevel() method for matricesmutate_edges() now coalesces rows of edgesgraph_blau_index()generate_permutation() and thus test_permutation()netlm() to network_reg() to avoid frustrating conflicts
network_reg() now accepts migraph-consistent objectsnetwork_reg() now accepts formula terms such as ego(), alter(), and same()generate_permutation() which takes an object and returns an object with the edges permuted, but retaining all nodal attributesgenerate_random() also work with an existing object as input, in which it will return a random graph with the same dimensions and densitymutate_edges() for adding new edges as attributes to existing edges in an objectgraph_blau_index()graph_ei_index()test_random() (defunct test_cug())autographr() arguments take variable names in quotation marksnode_names() for quickly accessing node labelsnode_attribute() for quickly accessing a certain nodal attributeedge_weights() for quickly accessing edge weightsgraph_nodes() for quickly accessing a count of nodes in the graph, note that for two-mode networks this will be a vector of length 2graph_edges() for quickly accessing a count of edges in the graphgraph_dimensions() is currently a copy of graph_nodes()add_node_attributes() for adding particular nodal attributesadd_edge_attributes() for adding edges from another graphcopy_edge_attributes() for copying all nodal attributes from another graphgraph_blau_index() for summarising diversity of an attribute in a network or groupgraph_ei_index() for summarising diversity of an attribute over a network’s tiesnode_quad_census(), especially useful for two-mode blockmodellinggraph_mixed_census()test_random() carries out a conditional uniform graph (CUG) testtest_permutation() carries out a quadratic assignment procedure (QAP) testaes() from {ggplot2}node_shape option to autographr()as_edgelist() methods for converting other objects into edgelists
to_unnamed() on ‘network’ objects now operates on them directlyto_ documentation significantlyto_onemode() that was tripping blockmodel() on networks that are already one-modeis_connected() to test whether network is connected, method = argument can be specified as weak or strongcreate_tree() and create_lattice(), and made create_star() a bit faster for one-mode networksgenerate_smallworld() and generate_scalefree(), though only for one-mode networks currently=2graph_dyad_census() for more graph profile optionsblockmodel_concor() when an object was of class ‘igraph’ but not ‘tbl_graph’blockmodel() was treating two-mode networks"elbow" and "strict" methods for k-identification
ggidentify_clusters() for speed
ggdistrib() for easy plotting of degree and other node score distributionsggsave(), xlab() and ylab() from {ggplot2} for easier plot annotationas_matrix() where availablegraph_equivalency() into the same for two-mode networks and graph_congruency()for three-mode (two two-mode) networksgraph_reciprocity() methodgraph_components() and node_components()node_tie_census() output so that it’s consistent with node_triad_census() and future node_census functionscluster_triad_census() to group_triad_census()group_tie_census()autographr() for plotting convex/concave hullsggraphgrid() a set of layout functions:
layout_tbl_graph_frgrid() or autographr(object, "frgrid") for snapping Fruchterman-Reingold to a gridlayout_tbl_graph_kkgrid() or autographr(object, "kkgrid") for snapping Kamada-Kawai to a gridlayout_tbl_graph_gogrid() or autographr(object, "gogrid") for snapping graph optimisation to a gridggraphgrid() has been deprecatedison_m182 documentationas_igraph()to_uniplex() was not returning a weighted graphblockmodel() was not retaining node names in all parts of the object structureplot_releases() to another packageis_signed() to logically test whether the network is a signed networkto_unsigned() for extracting networks of either “positive” or “negative” ties from a signed networktbl_graph methods for all other to_ functionsactivate() from {tidygraph}autographr()plot_releases() from this packagegraph_balance() to be much faster, following David Schoch’s {signnet} package (see that package for further extensions)ison_marvel_relationships to be a double (-1/1) to be compatible with the new graph_balance() and {signnet}{igraph} from data frames and updated read scriptto_main_component() to extract the main component of a networkto_onemode() for moving to multimodal igraph objectsto_uniplex() method to delete edge types and their edges from multiplex networksto_simplex() method to delete loops from a networkto_named() method for randomly naming unlabeled networksison_mm, ison_mb, ison_bm, and ison_bb projection illustration dataison_karateka community detection illustration dataison_marvel_teams and ison_marvel_relationships datasetsison_m182 dataset of friends, social and task ties between 16 anonymous studentsadolescent_society dataset to ison_colemanfor consistencygraph_eigenvector() for one mode networksgraph_balance() for measuring structural balancenode_tie_census(), node_triad_census(), cluster_triad_census(), and graph_triad_census()graph_clustering() into the cohesion measures graph_density(), graph_reciprocity(), graph_transitivity(), and graph_equivalence()node_smallworld() to use separated cohesion measuresblockmodel() which masks its {sna} namesake but has the advantages of working with two-mode networks and retaining node names where available
cluster_structural_equivalence() and cluster_regular_equivalence() as bases for blockmodellingreduce_graph() for creating a network from a blockmodelautographr() for plotting graphs with sensible defaults
ggraphlabel() since core functionality now provided by autographrggidentify() to identify the node with the highest value of a specified node-level measureggatyear() for subsetting and plotting edgelists at yeargglineage() to return a graph colored according to lineage
ggtree() for neatly visualising hierarchical clustersggidentify_clusters() for identifying which number of clusters is most appropriate following the elbow methodggraph::theme_graph() present in a few different visualisation functionsbrandes dataset for teaching centrality measuresadolescent_society dataset for teaching friendship paradoxread_edgelist() for importing Excel-created edgelists directlyggraphlabel() for one-function (1F) plotting label-based network graphsggevolution() for 1F-plotting begin/end graph comparisonsggraphgrid() for 1F snap-to-grid graph layouts based on Fruchterman-Reingold or Kamada-Kawaiggidentify() for 1F identifying nodes with maximum scores based on some arbitrary functionto_undirected() for symmetrising networks of all typesto_ functions S3 methodsClosed #100 by converting as_ coercion functions to S3 methods
as_matrix() weightingas_tidygraph()gglineage() for graphing a citation network through timeggevolution() for graphing two timepoints of the same network side by sideggraphgrid() for locking a graph to a gridplot.igraph() defaultsnode_ functionsFixed bug in as_matrix() with frame matrix by dropping (rarely necessary) functionality
as_ functionsFixed bugs in plot_releases() with more graceful handling of http errors
{R6}, {ggraph})netlm() testbinarise() to to_unweighted()to_unnamed() for unlabelling networksbinarise() for unweighting networksas_tidygraph() when passed a tbl_graph directlyplot_releases() for more general useplot.igraph() with layouts and one-mode graphsUpdated README
Added CITATION details
Separated coercion (previously conversion) and manipulation
Added some more inter-class coercion tests
Fixed bug in how as_network() sometimes coerced two-mode networks into much larger dimension matrices
Added more is_ tests for class-independent property tests
is_weighted()is_directed()is_labelled()Added @csteglich ’s read_ucinet() and write_ucinet() functions
read_ucinet() offers a file-picker when file path unknownread_ucinet() now imports to an igraph-class object by default, with an argument to allow other alternativeswrite_ucinet() works with all migraph-compatible objectsUpdated mpn_bristol documentation
Added create_star() function
directed = argumentcreate_ documentationRenamed sample_affiliation() to generate_random()
generate_random() to be able to generate random one- or two-mode networksprint.blockmodel()2021-04-13
usethis::use_mit_license("James Hollway"). MIT License file now contains only the standard two lines.\dontrun from examples. netlm() now runs in <5 seconds.2021-04-11
as_network() to coerce objects into network class2021-03-03
jhollway/ to snlab-ch/ organisationElaborated documentation for the remainder of the datasets
graph_degree() where data was hard-coded inClosed #18 by adding blockmodel_concor() for employing the CONCOR algorithm to blockmodel both one-mode and two-mode networks
print.blockmodel() method in the {sna} package that also prints blockmodel results for two-mode networks consistently{ggplot2} for pretty plotting and that better inherits names from the underlying object2021-02-06
{migraph} depend on R versions 4.0 or aboveas_matrix() function to coerce objects into an adjacency or incidence matrix classas_igraph() function to coerce objects into an {igraph} graph classas_tidygraph() function to coerce objects into an {tidygraph} tbl_graph classis_twomode() function to check whether network is two-mode on all object typesRenamed several datasets and elaborated their documentation
mpn_mexicanpower was renamed to mpn_elite_mexmpn_powerelite was renamed to mpn_elite_usa_advicempn_opensecrets was renamed to mpn_elite_usa_moneyReconstructed several creation functions to take universal (one-mode/two-mode) input: specifying n = 5 creates a one-mode network, while specifying n = c(5, 5) creates a two-mode network
create_empty()create_complete()create_ring() to create rings of varying breadthcreate_components() (renamed from create_silos()) to create networks with varying numbers of componentssample_affiliation() for random two-mode networkscreate_match() and create_nest()Renamed centrality_ functions with node_ prefix and ensured they all also wrapped one-mode measures
centrality_degree() renamed to node_degree()centrality_closeness() renamed to node_closeness()centrality_betweenness() renamed to node_betweenness()node_eigenvector()Re-added node_constraint() for calculating Burt’s constraint measure for one- and two-mode networks
Re-added node_smallworld() for calculating Watts-Strogatz measure of small-worldness for two-mode networks
Closed #32 by re-adding centralization functions for one- and two-mode networks
graph_degree() for degree centralizationgraph_closeness() for closeness centralizationgraph_betweenness() for betweenness centralizationRe-added graph_clustering() for calculating (see Knoke et al 2021):
Re-added netlm() for performing linear regression for multimodal network data
netlm() to accept a formula-based inputprint.summary.netlm() for netlm() regressionsplot.igraph() with sensible defaults for two-mode networks2021-01-11
{igraph} and {tidygraph} functions for internal useconvert_ and project_ documentationAdded centrality measures that take (and if necessary return) matrix, igraph, or tidygraph objects, and offer a correct normalization for two-mode networks
centrality_degree()centrality_closeness()centrality_betweenness()2021-01-08
Package name change from roctopus to {migraph}
Now builds Linux binary too
project_rows() and project_cols() to make it easier to project two-mode networks in different formats (matrix, igraph, tidygraph) into projected versions in the same formatas_incidence_matrix()2020-11-06
Data creation updated for {tidygraph} defaults
create_lattice() to create_chain() to avoid conflicts with tidygraph::create_lattice()create_poles() to create_silos()create_random() to play_twomode() to avoid conflicts with tidygraph::play_bipartite()create_ and play_ functions as tbl_graph (default), igraph, and base matrixcreate_ and play_ function namesPackaged data updated for {tidygraph} defaults
mpn_2020-08-18
twomode_netlm() to netlm2()netlm2()netlm2()create_random()opensecrets datapowerelite data2020-07-21
create_() functionscontext() declarationcreate_nest() now exports matrix object2020-07-20
row_project() and col_project() functions to make it easier to remember project directions2020-07-19
mexicanpower datasetsenate112 datasetsevs datasets to matricesdf_to_mat() function for converting regular adjacency and incidence data frames into matrices2020-07-17
ryanair dataset from Christopoulos 20062020-07-03
evs datasets from Knoke et al 20202020-06-30
bristol dataset from Knoke et al 20202018-12-20
twomode_modularity() to calculate modularity in two-mode networksplot_multilevel() that rotates a force-directed igraph plot in three dimensions to reveal multilevel structureplot_globalnet() to map a multilevel network on to a javascript, rotatable global2018-08-25
twomode_smallworld() to calculate observed/expected clustering, observed/expected path-length, and the observed/expected clustering ratiotwomode_2x2() to identify dominance and coherence values for networks over timetwomode_coherence() to allow for introduction of second-mode attributestwomode_fragmentation() to twomode_components()plot_2x2() to plot values through a two-by-two matrix2018-08-14
roctopustwomode_fragmentation() to calculate number of components in two-mode networks and identify their membershiptwomode_dominance() to allow an nodal attribute to be passed to the function to weight the centralization measuretwomode_coherence() to calculate Jaccard similarityAdded plot_twomode(), which wraps plot(igraph::graph_from_incidence_matrix()) with some useful defaults
2018-07-30
README.md file with instructions on how to install packageLICENSE file and pointed to bug/issue trackerNEWS.md file to track changes to the packagetwomode_clustering() to calculate percentage of three-paths closed by four-pathstwomode_lattice() to create two-mode latticestwomode_centralization_degree() to calculate degree centralization in two-mode networks, for rows, columns, or bothtwomode_centralization_between() to calculate betweenness centralization in two-mode networkstwomode_constraint() to calculate network constraint in two-mode networksarrange.vars() to rearrange variables by position