These results oered the rst evidence that large networks self-organize into a scale-free state, a feature unexpected by all existing random network models. To understand the origin of this scale invariance, BA have shown that existing network models fail to incorporate two key features of real networks: First, networks continuously grow by the addition of new vertices, and second, new vertices connect preferentially to highly connected vertices. Using a model incorporating these ingredients, they demonstrated that the combination of growth and preferential attachment is ultimately responsible for
the scale-free distribution and power-law scaling observed in real networks