easygraph.functions.basic package

Submodules

easygraph.functions.basic.avg_degree module

easygraph.functions.basic.avg_degree.average_degree(G) → float[source]

Returns the average degree of the graph.

Parameters: G (graph) – A EasyGraph graph
Returns: average degree – The average degree of the graph.
Return type: float

Notes

Self loops are counted twice in the total degree of a node.

Examples

>>> G = eg.Graph()  # or DiGraph, MultiGraph, MultiDiGraph, etc
>>> G.add_edge(1, 2)
>>> G.add_edge(2, 3)
>>> eg.average_degree(G)
1.3333333333333333

easygraph.functions.basic.cluster module

easygraph.functions.basic.cluster.average_clustering(G, nodes=None, weight=None, count_zeros=True, n_workers=None)[source]

Compute the average clustering coefficient for the graph G.

The clustering coefficient for the graph is the average,

\[C = \frac{1}{n}\sum_{v \in G} c_v,\]

where \(n\) is the number of nodes in G.

Parameters

G (graph) –
nodes (container of nodes, optional (default=all nodes in G)) – Compute average clustering for nodes in this container.
weight (string or None, optional (default=None)) – The edge attribute that holds the numerical value used as a weight. If None, then each edge has weight 1.
count_zeros (bool) – If False include only the nodes with nonzero clustering in the average.

Returns

avg – Average clustering

Return type

float

Examples

>>> G = eg.complete_graph(5)
>>> print(eg.average_clustering(G))
1.0

Notes

This is a space saving routine; it might be faster to use the clustering function to get a list and then take the average.

Self loops are ignored.

References

1: Generalizations of the clustering coefficient to weighted complex networks by J. Saramäki, M. Kivelä, J.-P. Onnela, K. Kaski, and J. Kertész, Physical Review E, 75 027105 (2007). http://jponnela.com/web_documents/a9.pdf
2: Marcus Kaiser, Mean clustering coefficients: the role of isolated nodes and leafs on clustering measures for small-world networks. https://arxiv.org/abs/0802.2512

easygraph.functions.basic.cluster.clustering(*args, **kwargs)

easygraph.functions.basic.localassort module

easygraph.functions.basic.localassort.localAssort(edgelist, node_attr, pr=array([0., 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]), undir=True, missingValue=- 1)[source]

Calculate the multiscale assortativity. You must ensure that the node index and node attribute index start from 0 :param edgelist: the network represented as an edge list,

i.e., a E x 2 array of node pairs