`igraphHRG`

objects can be printed to the screen in two forms: as
a tree or as a list, depending on the `type`

argument of the
print function. By default the `auto`

type is used, which selects
`tree`

for small graphs and `simple`

(=list) for bigger
ones. The `tree`

format looks like
this:

```
Hierarchical random graph, at level 3:
g1 p= 0
'- g15 p=0.33 1
'- g13 p=0.88 6 3 9 4 2 10 7 5 8
'- g8 p= 0.5
'- g16 p= 0.2 20 14 17 19 11 15 16 13
'- g5 p= 0 12 18
```

This is a graph with 20 vertices, and the
top three levels of the fitted hierarchical random graph are
printed. The root node of the HRG is always vertex group #1
(‘`g1`

’ in the the printout). Vertex pairs in the left
subtree of `g1`

connect to vertices in the right subtree with
probability zero, according to the fitted model. `g1`

has two
subgroups, `g15`

and `g8`

. `g15`

has a subgroup of a
single vertex (vertex 1), and another larger subgroup that contains
vertices 6, 3, etc. on lower levels, etc.
The `plain`

printing is simpler and faster to produce, but less
visual:

```
Hierarchical random graph:
g1 p=0.0 -> g12 g10 g2 p=1.0 -> 7 10 g3 p=1.0 -> g18 14
g4 p=1.0 -> g17 15 g5 p=0.4 -> g15 17 g6 p=0.0 -> 1 4
g7 p=1.0 -> 11 16 g8 p=0.1 -> g9 3 g9 p=0.3 -> g11 g16
g10 p=0.2 -> g4 g5 g11 p=1.0 -> g6 5 g12 p=0.8 -> g8 8
g13 p=0.0 -> g14 9 g14 p=1.0 -> 2 6 g15 p=0.2 -> g19 18
g16 p=1.0 -> g13 g2 g17 p=0.5 -> g7 13 g18 p=1.0 -> 12 19
g19 p=0.7 -> g3 20
```

It lists the two subgroups of each internal node, in as many columns as the screen width allows.

## See also

Other hierarchical random graph functions:
`consensus_tree()`

,
`fit_hrg()`

,
`hrg()`

,
`hrg-methods`

,
`hrg_tree()`

,
`predict_edges()`

,
`print.igraphHRGConsensus()`

,
`sample_hrg()`