In network science, a hub is a node with a number of links that greatly exceeds the average.
Emergence of hubs is a consequence of a scale-free property of networks.
The uprise of hubs in scale-free networks is associated with power-law distribution.
Hubs have a significantly larger number of links in comparison with other nodes in the network.
In random networks, the degree k is comparable for every node; it is therefore not possible for hubs to emerge.
In scale-free networks, nodes which emerged earlier have a higher chance of becoming a hub than latecomers.
This phenomenon is called first-mover advantage and it explains why some nodes become hubs and some do not.
However, in a real network, the time of emergence is not the only factor that influences the size of the hub.
Therefore, in real networks the growth and the size of a hub depends also on various attributes such as popularity, quality or the aging of a node.
In a scale-free network, hubs serve as bridges between the small degree nodes.
In an analysis of disease spreading or information flow, hubs are referred to as super-spreaders.
Super-spreaders may have a positive impact, such as effective information flow, but also devastating in a case of epidemic spreading such as H1N1 or AIDS.
The mathematical models such as model of H1N1 Epidemic prediction [6] may allow us to predict the spread of diseases based on human mobility networks, infectiousness, or social interactions among humans.
In a scale-free network hubs are most likely to be infected, because of the large number of connections they have.
Therefore, the selective immunization of hubs may be the cost-effective strategy in eradication of spreading disease.