O'Reilly logo

Statistical and Machine Learning Approaches for Network Analysis by Subhash C. Basak, Matthias Dehmer

Stay ahead with the world's most comprehensive technology and business learning platform.

With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, tutorials, and more.

Start Free Trial

No credit card required

6.2 Weighted Spectral Distribution

We now derive our metric, the weighted spectral distribution, relating it to another common structural metric, the clustering coefficient, before showing how it characterizes networks with different mixing properties.

Denote an undirected graph as G = (V, E) where V is the set of vertices (nodes) and E is the set of edges (links). The adjacency matrix of G, A(G), has an entry of one if two nodes, u and img, are connected and zero otherwise

(6.1) equation

Let img be the degree of node img and D = diag(sum(A)) be the diagonal matrix having the sum of degrees for each node (column of matrix) along its main diagonal. Denoting by I the identity matrix (I)i,j = 1 if i = j, 0 otherwise, the normalized Laplacian L associated with graph G is constructed from A by normalizing the entries of A by the node degrees of A as

(6.2) equation

or equivalently

(6.3) equation

As L is a real symmetric matrix ...

With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, interactive tutorials, and more.

Start Free Trial

No credit card required