High-Performance Open-Source Archive
traversal_weights(), node_weights(),
and all three main_path() types (global, local, key-route)
now run one vectorised step per topological layer instead of one
interpreted igraph call per vertex. On a 50,000-vertex / 300,000-arc
citation network the key-route extraction drops from tens of minutes to
a few seconds, with identical results (verified against Pajek and by
exhaustive path enumeration on small graphs).read_pajek() now parses arcs in a single vectorised
pass. The previous version built one data frame per arc and was
effectively O(E^2), taking minutes on large networks; it is now
sub-second.key_route_sweep(): computes the key-route main path for
many values of k in a single shared DP pass, returning a
tidy k / n_nodes / n_arcs table
(and optionally the subgraphs). This is the efficient way to choose
k by looking for a plateau, replacing a slow loop over
main_path().main_path(type = "key_route") gains a
k_range = c(start, end) argument to seed from a slice of
key routes ranked start to end by weight,
mirroring Pajek’s key-route range option.Initial CRAN release.
check_dag() validates that a graph is acyclic and
reports the back-edges responsible for any cycles.classify_nodes() labels vertices by role
(source/terminal/user, or source/sink/user under the graph-theory
convention).check_scale_free() fits a discrete power-law to the
degree distribution and tests for scale-free behaviour.edge_weights() and node_weights() compute
traversal weights at the edge and node level.read_pajek()) and Gephi export
(read_gephi_export(), supporting .gexf and
.graphml) files.plot_mpa() visualizes main paths, with a
scope argument for restricting the plot to a subgraph.igraph objects or edge-list data frames as
input throughout.
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