In general, clustering a Graph involves the grouping of components that
have many mutual or very strong dependencies (edges). One can also compute complete hierarchies by
clustering nodes in an iterative fashion (e.g. clusters of clusters).

You can tweak some parameters later, but at minimal it suffices to supply a
Graph. Let's apply the Markov clustering algorithm
ragraph.analysis.cluster.markov to the Ford Climate Control
System dataset:

Which shows that our graph has been clustered into three new nodes, with some default naming for
them. Lets review the hierarchy dictionary to view this in more detail:

The markov algorithm is a single-level clustering algorithm,
that will give you a single set of parent nodes (if any) for the given leaf nodes as input. If you
only supply a Graph it will use all the graph's leaf nodes.

An example of hierarchical clustering using the Hierarchical Markov clustering algorithm
ragraph.analysis.cluster.hierarchical_markov would be the
following:

fromragraphimportdatasetsfromragraph.analysisimportclusterg=datasets.get("climate_control")g,roots=cluster.hierarchical_markov(g,inplace=True)h=g.get_hierarchy_dict()asserth=={"node.node3":{"node.node0":{"Radiator":{},"Engine Fan":{},"Condenser":{},"Compressor":{},"Evaporator Core":{},"Accumulator":{},},"node.node1":{"Heater Core":{},"Heater Hoses":{},"Evaporator Case":{},"Actuators":{},"Blower Controller":{},"Blower Motor":{},},"node.node2":{"Refrigeration Controls":{},"Air Controls":{},"Sensors":{},"Command Distribution":{},},}}assertlen(roots)==1,"We found only one root."

Where we can see that all three earlier found clusters have now also been recursively grouped
together into a single root because of the interactions between their leaf nodes.