## Interfacing with the **igraph** R package

The **igraph** package (link) is the R
interface to the `igraph`

library (link) for network analysis. It
implements an extensive selection of algorithms for creating and generating directed and undirected graphs, manipulating
nodes and arcs, and it provides highly customizable plotting facilities. For this reason, **bnlearn**
implements functions to import and export network structures to **igraph**'s native objects, which are
themselves of class `igraph`

.

### Exporting a network structure to **igraph**

Exporting objects is achieved with the conversion method `as.igraph()`

.

> library(bnlearn) > > dag.bnlearn = random.graph(LETTERS[1:10]) > dag.bnlearn

Random/Generated Bayesian network model: [A][B][C][F][G][D|C][H|C:F][E|D][I|A:F:H][J|E] nodes: 10 arcs: 8 undirected arcs: 0 directed arcs: 8 average markov blanket size: 2.20 average neighbourhood size: 1.60 average branching factor: 0.80 generation algorithm: Full Ordering arc sampling probability: 0.2222222

> dag.igraph = as.igraph(dag.bnlearn) > dag.igraph

IGRAPH fc6706b DN-- 10 8 -- + attr: name (v/c) + edges from fc6706b (vertex names): [1] A->I C->D C->H D->E E->J F->H F->I H->I

`as.igraph()`

accepts objects of class `bn.fit`

as well as of class `bn`

, and it
implitly call `bn.net()`

(link) on the former.

> dag.test = hc(learning.test) > dag.test

Bayesian network learned via Score-based methods model: [A][C][F][B|A][D|A:C][E|B:F] nodes: 6 arcs: 5 undirected arcs: 0 directed arcs: 5 average markov blanket size: 2.33 average neighbourhood size: 1.67 average branching factor: 0.83 learning algorithm: Hill-Climbing score: BIC (disc.) penalization coefficient: 4.258597 tests used in the learning procedure: 40 optimized: TRUE

> fitted = bn.fit(dag.test, learning.test) > as.igraph(fitted)

IGRAPH 25a1eb4 DN-- 6 5 -- + attr: name (v/c) + edges from 25a1eb4 (vertex names): [1] A->B A->D B->E C->D F->E

Undirected arcs are exported as a pair of directed arcs, that is, X_{i} — X_{j} becomes
{X_{i} → X_{j}, X_{i} ← X_{j}}.

> as.igraph(skeleton(dag.test))

IGRAPH aa58703 DN-- 6 10 -- + attr: name (v/c) + edges from aa58703 (vertex names): [1] A->B A->D B->A B->E C->D D->A D->C E->B E->F F->E

### Importing a network structure from **igraph**

Importing network structures works in the same way, with a conversion method `as.bn()`

.

> as.bn(dag.igraph)

Random/Generated Bayesian network model: [A][B][C][F][G][D|C][H|C:F][E|D][I|A:F:H][J|E] nodes: 10 arcs: 8 undirected arcs: 0 directed arcs: 8 average markov blanket size: 2.20 average neighbourhood size: 1.60 average branching factor: 0.80 generation algorithm: Empty

Note that as graphs are imported with `as.bn()`

they are checked to be acyclic, unless
the user specifies `check.cycles = FALSE`

.

> library(igraph) > > cyclic.graph = make_graph(~ A-+B-+C-+D-+A) > cyclic.graph

IGRAPH 8929438 DN-- 4 4 -- + attr: name (v/c) + edges from 8929438 (vertex names): [1] A->B B->C C->D D->A

> as.bn(cyclic.graph)

## Error: the igraph object contains directed cycles.

> as.bn(cyclic.graph, check.cycles = FALSE)

Random/Generated Bayesian network model: [A|D][B|A][C|B][D|C] nodes: 4 arcs: 4 undirected arcs: 0 directed arcs: 4 average markov blanket size: 2.00 average neighbourhood size: 2.00 average branching factor: 1.00 generation algorithm: Empty

`Mon Aug 5 02:49:23 2024`

with **bnlearn**

`5.0`

and `R version 4.4.1 (2024-06-14)`

.