For the Barabási-Albert model, choose a connected seed graph on vertices, the number of new edges per added vertex, and a final size . A complete seed with suffices. Add one vertex at a time, attaching it to distinct existing vertices selected with weights proportional to their current vertex degrees. Exclude duplicate edges and self-loops.
Increasing creates a larger network and permits older hubs to grow. Increasing makes the graph denser, with asymptotic average vertex degree , and tends to shorten graph distances. The seed affects early structure and the oldest hubs; standard linear preferential attachment gives a robust large-size tail exponent.
Its asymptotic degree distribution is , so it is a scale-free network. Hubs produce short average shortest path length, but elevated clustering coefficients are not automatic: with a tree seed gives a triangle-free tree, and clustering decreases with size in the standard model. The strict clustered small-world network property is therefore not guaranteed.[ref]
Emergence of scaling in random networks by Albert-László Barabási and Réka Albert. pp. 510–511, growth, attachment and degree exponent
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