A Spatial Web Graph Model with Local Influence Regions

We present a new stochastic model for complex networks, based on a spatial embedding of the nodes, called the Spatial Preferred Attachment (SPA) model. In the SPA model, nodes have influence regions of varying size, and new nodes may only link to a node if they fall within its influence region. The spatial embedding of the nodes models the background knowledge or identity of the node, which will influence its link environment. In our model, nodes can determine their link environment based only on local knowledge of the network. We prove that our model gives a power law in-degree distribution, with exponent in [2,∞) depending on the parameters, and with concentration for a wide range of in-degree values. We show that the model allows for edges that span a large distance in the underlying space, modelling a feature often observed in real-world complex networks.