netneurotools.modularity.consensus_modularity
- netneurotools.modularity.consensus_modularity(adjacency, gamma=1, B='modularity', repeats=250, null_func=<function mean>, seed=None)[source]
Find community assignments from adjacency through consensus.
Performs repeats iterations of community detection on adjacency and then uses consensus clustering on the resulting community assignments.
- Parameters:
adjacency ((N, N) array_like) – Adjacency matrix (weighted/non-weighted) on which to perform consensus community detection.
gamma (float, optional) – Resolution parameter for modularity maximization. Default: 1
B (str or (N, N) array_like, optional) – Null model to use for consensus clustering. If str, must be one of [‘modularity’, ‘potts’, ‘negative_sym’, ‘negative_asym’]. Default: ‘modularity’
repeats (int, optional) – Number of times to repeat Louvain algorithm clustering. Default: 250
null_func (callable, optional) – Function used to generate null model when performing consensus-based clustering. Must accept a 2D array as input and return a single value. Default: np.mean
seed ({int, np.random.RandomState instance, None}, optional) – Seed for random number generation. Default: None
- Returns:
consensus ((N,) np.ndarray) – Consensus-derived community assignments
Q_all (array_like) – Optimized modularity over all repeats community assignments
zrand_all (array_like) – z-Rand score over all pairs of repeats community assignment vectors
References
Bassett, D. S., Porter, M. A., Wymbs, N. F., Grafton, S. T., Carlson, J. M., & Mucha, P. J. (2013). Robust detection of dynamic community structure in networks. Chaos: An Interdisciplinary Journal of Nonlinear Science, 23(1), 013142.
