| connect {igraph} | R Documentation |
These functions find the vertices not farther than a given limit from another fixed vertex, these are called the neighborhood of the vertex.
connect(graph, order, mode = c("all", "out", "in", "total"))
ego_size(
graph,
order = 1,
nodes = V(graph),
mode = c("all", "out", "in"),
mindist = 0
)
ego(
graph,
order = 1,
nodes = V(graph),
mode = c("all", "out", "in"),
mindist = 0
)
make_ego_graph(
graph,
order = 1,
nodes = V(graph),
mode = c("all", "out", "in"),
mindist = 0
)
graph |
The input graph. |
order |
Integer giving the order of the neighborhood. |
mode |
Character constant, it specifies how to use the direction of
the edges if a directed graph is analyzed. For ‘out’ only the
outgoing edges are followed, so all vertices reachable from the source
vertex in at most |
nodes |
The vertices for which the calculation is performed. |
mindist |
The minimum distance to include the vertex in the result. |
The neighborhood of a given order o of a vertex v includes all
vertices which are closer to v than the order. I.e. order 0 is always
v itself, order 1 is v plus its immediate neighbors, order 2
is order 1 plus the immediate neighbors of the vertices in order 1, etc.
ego_size() calculates the size of the neighborhoods for the
given vertices with the given order.
ego() calculates the neighborhoods of the given vertices with
the given order parameter.
make_ego_graph() is creates (sub)graphs from all neighborhoods of
the given vertices with the given order parameter. This function preserves
the vertex, edge and graph attributes.
connect() creates a new graph by connecting each vertex to
all other vertices in its neighborhood.
ego_size() returns with an integer vector.
ego() returns A list of igraph.vs or a list of numeric
vectors depending on the value of igraph_opt("return.vs.es"),
see details for performance characteristics.
make_ego_graph() returns with a list of graphs.
connect() returns with a new graph object.
Gabor Csardi csardi.gabor@gmail.com, the first version was done by Vincent Matossian
Other games:
erdos.renyi.game(),
sample_bipartite(),
sample_degseq(),
sample_dot_product(),
sample_gnm(),
sample_gnp(),
sample_grg(),
sample_growing(),
sample_hierarchical_sbm(),
sample_islands(),
sample_k_regular(),
sample_last_cit(),
sample_pa_age(),
sample_pa(),
sample_pref(),
sample_sbm(),
sample_smallworld(),
sample_traits_callaway()
Other structural.properties:
bfs(),
component_distribution(),
constraint(),
coreness(),
degree(),
dfs(),
diameter(),
distance_table(),
edge_density(),
feedback_arc_set(),
girth(),
is_matching(),
knn(),
laplacian_matrix(),
reciprocity(),
subcomponent(),
subgraph(),
topo_sort(),
transitivity(),
unfold_tree(),
which_multiple(),
which_mutual()
Other structural.properties:
bfs(),
component_distribution(),
constraint(),
coreness(),
degree(),
dfs(),
diameter(),
distance_table(),
edge_density(),
feedback_arc_set(),
girth(),
is_matching(),
knn(),
laplacian_matrix(),
reciprocity(),
subcomponent(),
subgraph(),
topo_sort(),
transitivity(),
unfold_tree(),
which_multiple(),
which_mutual()
Other structural.properties:
bfs(),
component_distribution(),
constraint(),
coreness(),
degree(),
dfs(),
diameter(),
distance_table(),
edge_density(),
feedback_arc_set(),
girth(),
is_matching(),
knn(),
laplacian_matrix(),
reciprocity(),
subcomponent(),
subgraph(),
topo_sort(),
transitivity(),
unfold_tree(),
which_multiple(),
which_mutual()
g <- make_ring(10)
ego_size(g, order = 0, 1:3)
ego_size(g, order = 1, 1:3)
ego_size(g, order = 2, 1:3)
ego(g, order = 0, 1:3)
ego(g, order = 1, 1:3)
ego(g, order = 2, 1:3)
# attributes are preserved
V(g)$name <- c("a", "b", "c", "d", "e", "f", "g", "h", "i", "j")
make_ego_graph(g, order = 2, 1:3)
# connecting to the neighborhood
g <- make_ring(10)
g <- connect(g, 2)