The Property Of Search Information In Spatial Networks

In a complex system, a well-organized topological structure ensure all activities go on wheels. In this paper, we aim at building up a network model that is searchable, and finding out the driver that leads to the topological structure. The main research of our work can be divided into two parts:In the first part. discussing the search information in complex networks. First, we study the search ability of urban street networks, and find the reason that it is more difficult to search in Wuhan than in Beijing is that Wuhan has more geographic boundaries, such as Yang Zi river, Han river, lakes and mountains. Then, we get the result that various kinds of real networks appear to be positively correlated by search information through the empirical study of dolphin, Beijing streets, protein-protein interaction in yeast, US power grid and co-authorship network. It is quite different from some typical network models. However, the regular lattice is almost linear. Learning from this, we reason that local restriction should be one of the factor that lead to this phenomenon.In the second part, we construct two-dimensional spatial networks, put every node of the network on the fixed coordinate position. The spatial distance is the difference between the nodes, action radius R reflect the local restriction in the network. Then we propose a class of models that depend on spatial distance. On the one hand, we get the consistent results of information correlation with the real one, which prove that spatial networks do show particularity while searching; on the other hand, we find that spatial distance plays a leading role in generating an assortative mixing by degree, while a disassortative mixing must rely on both degree preferential attachment and space restriction in our model.