Spatial networks are networks where nodes are located in a space equipped with a metric. Typically, the space is two-dimensional and until recently and traditionally, the metric that was usually considered was the Euclidean distance. In spatial networks, the cost of a link depends on the edge length, i.e. the distance between the nodes that define the edge. Hypothesizing that there is pressure to reduce the length of the edges of a network requires a null model, e.g. a random layout of the vertices of the network. Here we investigate the properties of the distribution of the sum of edge lengths in random linear arrangement of vertices, that has many applications in different fields. A random linear arrangement consists of an ordering of the...
We review and discuss the structural consequences of embedding a random network within a metric spac...
In this paper we introduce a new model of spatial network growth in which nodes are placed at random...
Many real-world networks of interest are embedded in physical space. We present a new random graph m...
Spatial networks are networks where nodes are located in a space equipped with a metric. Typically, ...
The minimum linear arrangement problem on a network consists of finding the minimum sum of edge leng...
6 pages, 1 figureWe study spatial networks constructed by randomly placing nodes on a manifold and j...
We consider the problem of determining the proportion of edges that are discovered in an Erdos-Rényi...
Andrew Wade Phase transitions for random spatial networks Spatial networks I Networks are everywhere...
The distribution of shortest path lengths (DSPL) of random networks provides useful information on t...
We review mathematically tractable models for connected networks on random points in the plane, emph...
We consider the problem of determining the proportion of edges that are discovered in a random graph...
We present two complementary analytical approaches for calculating the distribution of shortest path...
In this article, we explicitly derive the limiting distribution of the degree distribution of the sh...
In random geometric graphs, vertices are randomly distributed on [0,1]^2 and pairs of vertices are c...
AbstractWe study conditions under which the treewidth of three different classes of random graphs is...
We review and discuss the structural consequences of embedding a random network within a metric spac...
In this paper we introduce a new model of spatial network growth in which nodes are placed at random...
Many real-world networks of interest are embedded in physical space. We present a new random graph m...
Spatial networks are networks where nodes are located in a space equipped with a metric. Typically, ...
The minimum linear arrangement problem on a network consists of finding the minimum sum of edge leng...
6 pages, 1 figureWe study spatial networks constructed by randomly placing nodes on a manifold and j...
We consider the problem of determining the proportion of edges that are discovered in an Erdos-Rényi...
Andrew Wade Phase transitions for random spatial networks Spatial networks I Networks are everywhere...
The distribution of shortest path lengths (DSPL) of random networks provides useful information on t...
We review mathematically tractable models for connected networks on random points in the plane, emph...
We consider the problem of determining the proportion of edges that are discovered in a random graph...
We present two complementary analytical approaches for calculating the distribution of shortest path...
In this article, we explicitly derive the limiting distribution of the degree distribution of the sh...
In random geometric graphs, vertices are randomly distributed on [0,1]^2 and pairs of vertices are c...
AbstractWe study conditions under which the treewidth of three different classes of random graphs is...
We review and discuss the structural consequences of embedding a random network within a metric spac...
In this paper we introduce a new model of spatial network growth in which nodes are placed at random...
Many real-world networks of interest are embedded in physical space. We present a new random graph m...