# Moran Process on GraphsΒΆ

The library also provides a graph-based Moran process [Shakarian2013] with
`MoranProcess`

. To use this feature you must supply at least one
`Axelrod.graph.Graph`

object, which can be initialized with just a list of
edges:

```
edges = [(source_1, target1), (source2, target2), ...]
```

The nodes can be any hashable object (integers, strings, etc.). For example:

```
>>> import axelrod as axl
>>> from axelrod.graph import Graph
>>> edges = [(0, 1), (1, 2), (2, 3), (3, 1)]
>>> graph = Graph(edges)
```

Graphs are undirected by default but you can pass `directed=True`

to
create a directed graph. Various intermediates such as the list of neighbors
are cached for efficiency by the graph object.

A Moran process can be invoked with one or two graphs. The first graph, the
*interaction graph*, dictates how players are matched up in the scoring phase.
Each player plays a match with each neighbor. The second graph dictates how
players replace another during reproduction. When an individual is selected to
reproduce, it replaces one of its neighbors in the *reproduction graph*. If only
one graph is supplied to the process, the two graphs are assumed to be the same.

To create a graph-based Moran process, use a graph as follows:

```
>>> from axelrod.graph import Graph
>>> edges = [(0, 1), (1, 2), (2, 3), (3, 1)]
>>> graph = Graph(edges)
>>> players = [axl.Cooperator(), axl.Cooperator(), axl.Cooperator(), axl.Defector()]
>>> mp = axl.MoranProcess(players, interaction_graph=graph, seed=40)
>>> results = mp.play()
>>> mp.population_distribution()
Counter({'Defector': 4})
```

You can supply the `reproduction_graph`

as a keyword argument. The
standard Moran process is equivalent to using a complete graph with no loops
for the `interaction_graph`

and with loops for the
`reproduction_graph`

.