In a subcritical regime the network has no giant component, only small clusters. In the special case of ${\displaystyle \langle k\rangle =0}$  the network is not connected at all. A random network is in a subcritical regime until the average degree exceeds the critical point, that is the network is in a subcritical regime as long as

${\displaystyle \langle k\rangle <1}$ .^[3]

In a supercritical regime, in contrary to the subcritical regime the network has a giant component. In the special case of ${\displaystyle \langle k\rangle =N-1}$  the network is complete (see complete graph). A random network is in a supercritical regime if the average degree exceeds the critical point, that is if

${\displaystyle \langle k\rangle >1}$ .^[3]

Consider a speed dating event as an example, with the participants as the nodes of the network. At the beginning of the event, people do not know anyone else. In this case the network is in a subcritical regime, that is, there is no giant component in the network (even if there are a couple of people, who know each other). After the first round of dates, everyone knows exactly one other person. There is still no giant component in the network, the average degree is ${\displaystyle \langle k\rangle =1}$ , that is, everyone knows one other person on average, meaning that the network is at the critical point. After the second round, the average degree of the network exceeds the critical point, and the giant component is present. In this specific case, the average degree is ${\displaystyle \langle k\rangle =2}$ . The network is in a supercritical regime.

• Graph theory
• Percolation theory
• Complex network
• Random graph