pymnet.lcc_aw

pymnet.lcc_aw(net, node, layer, w1=0.5, w2=0.5, w3=None, returnCVector=False, anet=None)

The local version of the alternating walker clustering coefficient for multiplex networks.

Parameters:

netMultiplexNetwork with aspects=1

The input network.

nodeany object

The focal node. Given as node index in the network.

layerany object

The focal layer. Given as layer index in the network.

w1,w2,w3weights of the contributions of different layers.

If w3 is set to None then w1 and w2 correspond to the “costs” of staying at a layer and changing the layer, i.e. w1 = \(\beta\) and w2 = \(\gamma\)

returnCVectorbool

If True, returns a vector containing the three different local clustering coefficients \(c_1,c_2,c_3\). Otherwise, return just a single value.

Returns:

ccfloat, or tuple

The value(s) of the clustering coefficient.

See also

avg_lcc_aw

The local alternating walks clustering coefficient averaged over all node-layer pairs.

sncc_aw

The super-node version of the alternating walks clustering coefficient.

gcc_aw

The global version of the alternating walks clustering coeffient.

References

“Clustering Coefficients in Multiplex Networks”, E. Cozzo et al. , arXiv:1307.6780 [physics.soc-ph]