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Changelog

Source: NEWS.md

netmem 1.1-0

Changes that affect results

These fixes change the numbers that some functions returned. Code written for version 1.0-3 runs without errors, but gives different results:

  • count_geodesics() counted only some of the geodesics between two nodes. A node that had already been reached did not add the geodesics of the other nodes at the same distance, so the counts were too low. The counts now agree with sna::geodist().

  • wall_distances() returned a single path for each node instead of one path for each pair of nodes. It now returns fromTo[[i]][[j]] and toFrom[[i]][[j]] for every pair.

  • wlocal_distances() and wall_distances() now use Dijkstra’s algorithm, as the documentation said. Before they enumerated every simple path, which did not finish on networks of a dozen nodes. Between paths of the same length, the path returned might differ from the one returned before.

  • triad_uman(): the covariance between the counts of 201 and 102 was positive, and should have been negative. The term of the two triads that share a dyad used m + n + 4 where the combinatorics give m + n - 4. The covariance matrix now adds up to zero, as it must because the number of triads is fixed.

  • triad_uman() no longer returns the element z_test by default. It tested the sum of the sixteen counts, which is always choose(g, 3), so it was NaN for every network. The new argument l tests a linear combination of the census (Wasserman and Faust, 1994: 583); with ztest = TRUE and no l, the function returns the data frame with the columns Z and P. The covariance matrix is now returned symmetric instead of triangular.

  • eb_constraint() failed for egos with a single alter, and for egos whose alters are not tied to each other. It now returns the constraint in both cases, and gives an informative error for isolates.

  • components_id() did not return the components of a directed network. It grouped the nodes that reach the same nodes, so a chain of citations gave as many components as nodes. It now returns the weak components, which for undirected networks are the ones it returned before, and takes the arguments mode = "strong" and bipartite.

  • edgelist_to_matrix() placed some ties in the wrong direction, depending on the order in which the nodes appeared in the edge list, and dropped the ties of a node with itself. The names are now matched one by one, and loops = TRUE keeps the diagonal. It also returned a vector instead of a matrix when the result had a single row or column.

  • mix_matrix() counted only the ties of the upper triangle of the matrix, so for a directed network with 15 arcs it returned a table adding up to 5, and it dropped the row or the column of a group that never sent or received a tie, which made the table lose its shape. The mixing matrix is now built from the ties between every pair of groups.

  • ei_index() ignored the attribute unless mixed = FALSE was given, and with its default arguments it returned one for any network without loops. The attribute now takes precedence, and the index agrees with netseg::ei() for directed and undirected networks.

  • trans_matrix() failed with “the condition has length > 1” whenever a node belonged to more than one transitive triple, which is the usual case in an undirected network with a triangle. It now marks the nodes of every transitive triple.

  • triad_uman() failed on a network without ties, because the matrix was stored in a class that cannot be modified.

  • multilevel_degree() returned wrong values for the nodes of the third level when B2 was given without B3: the rows k1, k2, ... of the column multilevel held the degrees of the nodes of the second level. They now count the ties of each node within the third level and with the second level, which is the value of the same nodes in high_multilevel, and the documentation describes what every column counts.

  • gen_degree() with weighted = TRUE removed the loops from the degree but not from the strength when loops = FALSE, and symmetrized the ties but not the weights when digraph = FALSE. The weights now follow the ties in both cases.

  • gen_density() removed the diagonal of two-mode matrices, which holds real ties, so with the default loops = FALSE the density of an incidence matrix was too low (its own example gave 0.33 instead of 0.5). For a directed matrix in a list (multilayer = TRUE) it used only the lower triangle, and with directed = FALSE it kept the ties of the upper triangle instead of the underlying graph. With loops = TRUE the diagonal is now counted among the possible ties. The results agree with igraph::edge_density().

  • eb_constraint() chose the maximum constraint by the size of the ego network including ego, so an ego with seven alters got the maximum of the shadow ego network (0.486) instead of the complete one (0.493, Everett and Borgatti, 2020: Table 1), and a normalization that was too high. The maximum is now the larger of the two. An ego with a single alter has a normalization of one, as in Everett and Borgatti (2020: Eq. 5), instead of NaN.

  • q_analysis() reported only the dimensions q that some simplex had exactly, so the levels at which components merge were missing: two 4-simplices that share a face of dimension 2 were never reported as 2-connected. It now reports every q from the largest dimension down to 0. A network was turned into a complex of its triangles and all its edges, so a clique of four nodes became four triangles; the simplices are now the maximal cliques. A square incidence matrix, which stopped the function, is now accepted. The function returns a list with the simplices, the table of the structure vectors, the components at each q and the eccentricities; the argument dimensions is kept but no longer needed. The values agree with Freeman (1980) and with the Python package of Smirnov et al. (2025).

  • simplicial_complexes() built the complex of a network from its triangles and all its edges, so a clique of four nodes became four triangles and six edges, and it stopped with an error on networks without triangles. The simplices are now the maximal cliques, named after their nodes (a-b-c) instead of numbered. zero_simplex = TRUE, now the default, adds the isolated nodes as simplices of dimension 0; before it added columns that did not correspond to the nodes. The rows are still the nodes and the columns the simplices.

  • k_core() of a binary network counted the loops even when loops = FALSE, which is the default. The loops are now counted only with loops = TRUE. Without loops the values are the same as before.

  • zone_sample() returns adjacency matrices instead of igraph objects. With core = TRUE, the indicator of the actors at distance one is the attribute core of each matrix. The nodes and the ties are the same as before.

  • An audit of every exported function found further errors, recorded in dev/audit/audit.md with how each was verified:

    • multiplex_census() added counts of the two networks instead of counting the triples of each joint configuration (two empty networks gave 5 of 10 triples). It now returns the classes of Figure 12 of Espinosa-Rada (2021), named by the type of the directed triad and the position of the undirected edges (e.g. 021U_102ac); merge = "overlap" merges the classes that give the same overlapped triad.
    • mixed_census(quad = TRUE): the class “201” multiplied two counts instead of adding them.
    • kp_reciprocity() used the number of arcs minus twice the mutual dyads instead of the number of arcs for free choices.
    • posneg_index(): select = "all" returned the out-index and "out" the all-index. They now agree with signnet::pn_index().
    • k_core() with weighted = TRUE or multilevel = TRUE returned the round in which a node was removed instead of its core value (a path gave 1 0 1).
    • With digraph = FALSE, gen_degree(), eb_constraint(), redundancy(), clique_table(), dyad_triad_table(), struc_balance() and the signed eigenvector_centrality() copied the upper triangle of the matrix over the lower one, so the ties present only in the lower triangle were lost and the results depended on the order of the nodes. They now use the underlying graph.
    • edgelist_to_matrix(digraph = FALSE) dropped the edges listed as (b, a); matrix_to_edgelist(valued = TRUE) dropped ties with non-integer values and listed undirected ties twice; matrix_adjlist() dropped ties below 1.
    • adj_to_matrix(type = "adjacency") returned one row per line of the list instead of one per node.
    • meta_matrix() placed the ties between the second and third levels only above the diagonal, and dropped them when B3 was given.
    • extract_component() returned wrong matrices when several components had the same size; position now counts the distinct sizes.
    • dist_sim_matrix() computed the Hamming distance of two-mode matrices over the wrong nodes, failed with rows without ties, and now keeps the names of the nodes.

  • fractional_approach() did not compute the networks of Batagelj (2020): its “citation” network was a product of the two incidence matrices, and the fractional co-citation was normalised on one side only. It now takes the citation network between works (A1) and, for the citations between authors, the authorship matrix (A2), with full or fractional counting (fractional); the fractional bibliographic coupling can be made symmetric with the six measures of Batagelj (2020) (symmetric). Calls with two incidence matrices, as in version 1.0-3, now stop.

  • dyad_triad_table() called “Triad201” every pair of neighbours of a node, closed or not, and min and max limited how often a triad was repeated (which selected the closed triads). It now returns the type of each triad (201 for the forbidden triad of Granovetter, 300 when closed, 102 for a dyad and 003 for an isolated node) and its members, and min and max limit the number of forbidden triads of which a node is the centre.

Other bug fixes

Extensions of the existing functions

  • ind_rand_matrix() takes sparse, which draws the ties among the cells that the model allows and returns a sparse matrix of the Matrix package, without building the dense one. A network of 10,000 nodes takes 0.2 MB instead of 800 MB. It is available for one-mode and two-mode networks with trials = 1.

  • edgelist_to_matrix() orders the nodes as in label (and label2 for two-mode networks), with the nodes that are not in the labels after them in alphabetical order. Before, the nodes were always in alphabetical order, so a matrix could not be recovered from its edge list in its own order.

  • components_id() takes mode (weak or strong) and bipartite, which covers the nodes of both modes (issue #4).

  • eigenvector_centrality() takes signed, for networks with negative ties (Bonacich and Lloyd, 2004) (issue #9).

  • trans_coef() takes method = "barrat", the weighted transitivity of Barrat et al. (2004) (issue #5).

  • gen_density() computes the density of weighted networks, which is the average strength of the possible ties (issue #12).

  • edgelist_to_matrix() takes valued, which reads the value of the ties from a third column, and loops (issue #2).

  • dist_sim_matrix() takes a list of matrices, and compares the nodes across all the relations at once (issue #7).

  • q_analysis() returns the second and third structure vectors (Raj et al., 2024), the obstruction vector and the eccentricity of each simplex, with the definition of Atkin (1974) or of Johnson (eccentricity). A network can be analysed through its clique complex or its neighbourhood complex (complex), with open or closed neighbourhoods (closed). It is also about a hundred times faster.

  • simplicial_complexes() takes complex = "neighbourhood" and closed, as q_analysis(), which now builds its complex with it, and valued for projections that count the shared nodes and simplices.

  • eb_constraint() takes digraph = TRUE and weighted = TRUE, which used to stop. A directed network has the constraint of the valued network A + t(A), and the maximum used in the normalization is that of Everett and Borgatti (2020: Eq. 6 to 9). The results agree with their Tables 1 and 2 and with igraph::constraint() on the ego network.

  • edgelist_to_matrix() takes rule for undirected networks: a tie listed in either order (weak, default) or only in both orders (strong), as sna::symmetrize().

  • gen_degree() and gen_density() no longer warn that a symmetric matrix is undirected when digraph = TRUE (or directed = TRUE), as the result is the same.

New functions

Positions and dominance:

Roles, positions and macro structure:

Communities:

Inference:

  • cug_test() for conditional uniform graphs, and qap_cor() and qap_lm() for the QAP correlation and the MRQAP regressions, linear and logistic.

Dynamics and generators:

Segregation:

  • segregation() with the five measures reviewed by Bojanowski and Corten (2014).

Overlapping categories (Everett and Borgatti, 2026):

  • alter_composition(), alter_heterogeneity(), alter_homophily() (E-I index and Yule’s Q), brokerage_roles() (Gould and Fernandez) and partition_centrality() for nodes that belong to several categories, given as a membership matrix that is made row-stochastic, or as a vector when the categories are a partition. alter_homophily() takes similarity for the three definitions of the similarity of two memberships (product, minimum and cosine).
  • structural_holes() with the effective size, efficiency and constraint of Burt for every node of a valued or directed network, computed within the ego networks (as UCINET) or the whole network (as igraph), and with the option of treating the alters of the same category as redundant (B and beta).
  • The results agree with Tables 1, 3, 4, 5, 6 and 10 of the article, with sna::brokerage() and igraph::constraint(). Tables 8 and 9 are reproduced within rounding when Chuck spends 12 of 51 hours on the third task instead of 11 of 44 as printed in Table 2, which suggests that they were computed with those hours.
  • The dataset campnet, the Camp 92 network with the gender and role of each person.

Citation networks:

  • traversal_weights() with the search path count (SPC), the search path link count (SPLC) and the search path node pair (SPNP) of each arc, and the weighted in-degree and out-degree of each paper (Kuan, 2020). The weights agree with the published values of Liu and Lu (2012, Fig. 1) and Kuan (2020, Tables 3 and 4).
  • main_path() with the global, local and key-route main paths (Liu and Lu, 2012), main_path_diag(), citation_decay(), and dag_check() and dag_sort() to remove the cycles of a citation network and to order it.

Notes

  • The documentation of every exported function is checked by dev/audit/03_documentation.R: that each one says what it returns, that the value is not copied from another function, that the arguments of the documentation and of the function are the same, that the cross-references point to topics that exist, and that every DOI resolves and belongs to the reference that cites it. It corrected the value of ind_rand_matrix(), which said that it returned a dyad census, and made the value of neigh_inclusion(), dir_inclusion() and pos_dominance() say what their matrices mean.
  • Two new vignettes: Getting started with netmem, with the standard analysis of a network, and What netmem adds, with the measures that are not available elsewhere (neighbourhood-inclusion dominance, overlapping categories, Q-analysis, main paths, fractional counting and hyper-event dominance). The vignette Multilayer networks uses the new multiplex_census() and corrects two references.
  • netmem no longer imports igraph. k_core() and zone_sample() were the only functions that used it. igraph is in Suggests, for the plots of the vignette.
  • The new functions do not use igraph. Their results are compared with igraph, sna, netrankr and netseg in dev/validation, which is not part of the package.
  • leiden(), community_label() and core_periphery() start from random partitions, so they need a seed to be reproducible.
  • Functions that could mask those of other packages were named to avoid it: clique_max(), pos_dominance(), indirect_rel(), dominance_ranks(), segregation() and centrality_centralization() (sna::centralization()).

netmem 1.0-3

CRAN release: 2026-04-16

  • Version on CRAN.