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method [17]: For a randomly generated network, pick a random initial state S$_{i}$ , and let it run for a large number of steps t$_{max}$ ( t$_{max}$ = 10000 in our present experiments), to reach a final state S$_{f}$ . Now, apply a random point “mutation” to initial state S$_{i}$ to obtain S ′ $_{i}$, i.e. do a random bit flip. Then, let the network run for t$_{max}$ from S ′ i to obtain another final state S ′ $_{f}$. The difference between states can be calculated with the normalized Hamming distance: 

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If states A and B are equal, then H ( A,B ) = 0. The maximum H = 1 is given when A is the complementary state of B , i.e. every node with state one in A has a state zero in B and every node with state zero in A has a state one in B , i.e. full anticorrelation. H = 0 . 5 implies no correlation between A and B . The smaller H is, the more similar A and B are. As H increases (up to H = 0 . 5), 

