`network.cycle(size, b, node=dynNode, edge=weightedEdge)`- Adds a closed chain to the network in which every node is connected to
`b`neighbours on each side.

`size`: integer- Number of nodes in the chain
`b`: integer- Each node is connected to its
`b`nearest neighbours on each side. `node`: node template- Every added node is a copy of this template.
`edge`: edge template- Every added edge is a copy of this template.

The number of the first added node. The following nodes have consecutive numbers.

```
import conedy as co
co.setRandomSeed(0)
N = co.network()
N.cycle(1000,50, co.node(), co.weightedEdge()) # Creates a closed chain of 1000 nodes where each is connected to its 50 nearest neighbors to each side.
print "should be close to 0.75:" + str ( N.meanClustering() )
print "should be close to " + str (1000.0/ 2 / 100) +":" + str ( N.meanPathLength() )
print "should be 100:" + str ( N.meanDegree() )
```

```
network N;
outputPrecision = 4;
N.cycle(1000,50, node(), weightedEdge()); # Creates a closed chain of 1000 nodes where each is connected to its 50 nearest neighbors to each side.
print "should be close to 0.75:" + ( N.meanClustering() ) + newline;
print "should be close to " + (1000.0/ 2 / 100) +":" + ( N.meanPathLength() ) + newline;
print "should be 100:" + N.meanDegree() + newline;
```