  Inhomogeneity in networks can be detected by the analysis of the correlation
of the total degree of nearest neighbors. This is illustrated by two models.
The first one is a random multi-partitions network that the Aboav Weaire law,
which predicts the linear relationship between the degree of node and the total
degree of nearest neighbor, is being extended. The second one is a preferential
attachment network with two partitions which shows scale free properties with
power tail $\gamma$ within the range $2<\gamma\le3$. By plotting the total
degree of neighbor verses the degree of each node in the networks, the
scattered plot shows separable clustering as evidence for inhomogeneity in
networks. The effectiveness of this new tool for the detection of inhomogeneity
is demonstrated in real bipartite networks. By using this method, some
interesting group of nodes of semantic and WWW networks have been found.
