networkx.algorithms.non_randomness.non_randomness¶
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non_randomness
(G, k=None)[source]¶ Compute the non-randomness of graph G.
The first returned value nr is the sum of non-randomness values of all edges within the graph (where the non-randomness of an edge tends to be small when the two nodes linked by that edge are from two different communities).
The second computed value nr_rd is a relative measure that indicates to what extent graph G is different from random graphs in terms of probability. When it is close to 0, the graph tends to be more likely generated by an Erdos Renyi model.
Parameters: - G (NetworkX graph) – Graph must be binary, symmetric, connected, and without self-loops.
- k (int) – The number of communities in G. If k is not set, the function will use a default community detection algorithm to set it.
Returns: non-randomness – Non-randomness, Relative non-randomness w.r.t. Erdos Renyi random graphs.
Return type: Examples
>>> G = nx.karate_club_graph() >>> nr, nr_rd = nx.non_randomness(G, 2)
Notes
This computes Eq. (4.4) and (4.5) in Ref. [1].
References
[1] Xiaowei Ying and Xintao Wu, On Randomness Measures for Social Networks, SIAM International Conference on Data Mining. 2009