Two preferential attachment-type graph models which allow for dynamic addition/deletion of edges/vertices are considered. The focus of this paper is on the limiting expected degree of a fixed vertex. For both models a phase transition is seen to occur, i.e. if the probability with which edges are deleted is below a model-specific threshold value, the limiting expected degree is infinite, but if the probability is higher than the threshold value, the limiting expected degree is finite. In the regime above the critical threshold probability, however, the behaviour of the two models may differ. For one of the models a non-zero (as well as zero) limiting expected degree can be obtained whilst the other only has a zero limit. Furthermore, this p...
We analyse the threshold network model in which a pair of vertices with random weights are connected...
In the area of complex networks so far hypergraph models have received significantly less attention ...
We consider an evolving preferential attachment random graph model where at discrete times a new nod...
Two preferential attachment-type graph models which allow for dynamic addition/deletion of edges/ver...
We study a model of grown graph where a vertex is added at each time step, then an edge is added wi...
In this paper a discrete-time dynamic random graph process is studied that interleaves the birth of ...
The effect of deletion of old edges in the preferential attachment model introduced by Barabasi and ...
In this paper a discrete-time dynamic random graph process is studied that interleaves the birth of ...
AbstractThis paper focuses on the degree sequence of a random graph process with copying and vertex ...
Preferential attachment is a popular model of growing networks. We consider a generalized ...
In this paper we study the degree distribution and the two-node degree correlations in growing netwo...
Percolation or cascades on random networks are typically analyzed using generating functions. This a...
In this paper, a random graph process {G(t)} (ta parts per thousand yen1) is studied and its degree ...
In this paper, a random graph process {G(t)}t≥1 is studied and its degree sequence is analyzed. Let ...
In a 2-parameter scale free model of random graphs it is shown that the asymptotic degree distributi...
We analyse the threshold network model in which a pair of vertices with random weights are connected...
In the area of complex networks so far hypergraph models have received significantly less attention ...
We consider an evolving preferential attachment random graph model where at discrete times a new nod...
Two preferential attachment-type graph models which allow for dynamic addition/deletion of edges/ver...
We study a model of grown graph where a vertex is added at each time step, then an edge is added wi...
In this paper a discrete-time dynamic random graph process is studied that interleaves the birth of ...
The effect of deletion of old edges in the preferential attachment model introduced by Barabasi and ...
In this paper a discrete-time dynamic random graph process is studied that interleaves the birth of ...
AbstractThis paper focuses on the degree sequence of a random graph process with copying and vertex ...
Preferential attachment is a popular model of growing networks. We consider a generalized ...
In this paper we study the degree distribution and the two-node degree correlations in growing netwo...
Percolation or cascades on random networks are typically analyzed using generating functions. This a...
In this paper, a random graph process {G(t)} (ta parts per thousand yen1) is studied and its degree ...
In this paper, a random graph process {G(t)}t≥1 is studied and its degree sequence is analyzed. Let ...
In a 2-parameter scale free model of random graphs it is shown that the asymptotic degree distributi...
We analyse the threshold network model in which a pair of vertices with random weights are connected...
In the area of complex networks so far hypergraph models have received significantly less attention ...
We consider an evolving preferential attachment random graph model where at discrete times a new nod...