paramnet 2.3.0

NetworkX graphs with required node/edge parameters

paramnet provides a convenience mixin, Parametrized, for creating subclasses of NetworkX’s Graph (DiGraph) that have numeric parameters associated with nodes and edges (for example, a dynamical system on a network). It provides the following key features:

• Simplified reads/writes for the parameter value for a specific node/edge, without navigating NetworkX’s nested dict structure

• Enforcing a well-defined node order on the graph, which in turn allows…

• Extraction of all values of a given node (edge) parameter as a 1D (2D) numpy array

Examples

A parametrized network class can be created by subclassing from Graph or DiGraph using the Parametrized mixin. The names of any node/edge parameters can be specified in the class definition:

>>> class MyGraph(Parametrized, Graph, node_params=['x'], edge_params=['y']):
...    pass

G = MyGraph()

Note: As with all mixins, Parametrized should be inherited from first as above.

paramnet automatically adds named fields for each declared parameter, supporting clean random access (both read and write) by node or edge

>>> G.add_edge('a', 'b', y=3)
>>> G.x['a']
100
>>> G.x['a'] = 4
>>> G.y['a', 'b']
3
>>> G.y['a', 'b'] = 50

Contrast this with the more cumbersome random access in base NetworkX (which can be used interchangeably if you wish):

>>> G.nodes['a']['x']
100
>>> G.nodes['a']['x'] = 4
>>> G.edges['a', 'b']['y']
3
>>> G.edges['a', 'b']['y'] = 50

What’s more, paramnet maintains the order in which nodes were added, allowing index lookup:

>>> G.index('a')
0
>>> G.index('b')
1
>>> G.add_node('c')
>>> G.index('c')
2

But that’s not all. Because the nodes are ordered, we can get a well-defined representation of all values of a given node (edge) parameter at once as a vector (matrix). This is accomplished within paramnet by accessing the associated parameter attribute without square brackets:

>>> G.x
array([100, 100])
>>> G.y
array([[0., 3.],
       [3., 0.]])

>>> G.A
array([[0., 1.],
       [1., 0.]])

Note the special case for the weighted network adjacency matrix, which is automatically defined for every graph through the field A (instead of weight), regardless of whether weight is supplied in edge_params.

Under the hood, the parameter fields return View objects that wrap most numpy functionality, allowing vector arithmetic and other array operations

>>> G = MyGraph()
>>> G.add_nodes_from([(node, {'x': 5*node+1}) for node in range(5)])
>>> G.add_cycle(range(5), y=1)

# number of paths of length two between node pairs
>>> np.dot(G.A, G.A)
array([[0., 3., 1., 1., 3.],
       [3., 0., 3., 1., 1.],
       [1., 3., 0., 3., 1.],
       [1., 1., 3., 0., 3.],
       [3., 1., 1., 3., 0.]])

>>> G.x + 1
array([ 2,  7, 12, 17, 22])

Dependencies

• NetworkX (>= 2.0)

• numpy

License

paramnet is released under the MIT license. See LICENSE for details.