A model for growing information networks is introduced where nodes receive new links through j-redirection, i.e. the probability for a node to receive a link depends on the number of paths of length j arriving at this node. In detail, when a new node enters the network, it either connects to a randomly selected node, or to the j-ancestor of this selected node. The j-ancestor is found by following j links from the randomly selected node. The system is shown to undergo a transition to a phase where condensates develop. We also find analytical predictions for the height statistics and show numerically the non-trivial behaviour of the degree distribution
Our work introduces an approach for estimating the contribution of attachment mechanisms to the for...
We propose a new preferential attachment–based network growth model in order to explain two properti...
There is a growing interest in the study of graphs that evolve over time. Communication networks, pe...
A model for growing information networks is introduced where nodes receive new links through j-redir...
We investigate the growth of connectivity in a network. In our model, starting with a set of disjoin...
Growing networks have a causal structure. We show that the causality strongly influences the scaling...
In present study, I present a method of network evolution that based on random network, and facilita...
Based on the analysis of the dependency network in 18 Java projects, we develop a novel model of net...
Complex networks describe a wide range of systems and structures in the world. Any real network can ...
We present a simple model of network growth and solve it by writing the dynamic equations for its ma...
Many complex networks from the World Wide Web to biological networks grow taking into account the he...
We present a model for growing information networks where the ageing of a node depends on the time a...
a. Assume the network grows as described in [1, Fig. 3b], by duplication of a gene (node) chosen uni...
One of the best-known models in network science is preferential attachment. In this model, the proba...
We examine the global organization of growing networks in which a new vertex is attached to already ...
Our work introduces an approach for estimating the contribution of attachment mechanisms to the for...
We propose a new preferential attachment–based network growth model in order to explain two properti...
There is a growing interest in the study of graphs that evolve over time. Communication networks, pe...
A model for growing information networks is introduced where nodes receive new links through j-redir...
We investigate the growth of connectivity in a network. In our model, starting with a set of disjoin...
Growing networks have a causal structure. We show that the causality strongly influences the scaling...
In present study, I present a method of network evolution that based on random network, and facilita...
Based on the analysis of the dependency network in 18 Java projects, we develop a novel model of net...
Complex networks describe a wide range of systems and structures in the world. Any real network can ...
We present a simple model of network growth and solve it by writing the dynamic equations for its ma...
Many complex networks from the World Wide Web to biological networks grow taking into account the he...
We present a model for growing information networks where the ageing of a node depends on the time a...
a. Assume the network grows as described in [1, Fig. 3b], by duplication of a gene (node) chosen uni...
One of the best-known models in network science is preferential attachment. In this model, the proba...
We examine the global organization of growing networks in which a new vertex is attached to already ...
Our work introduces an approach for estimating the contribution of attachment mechanisms to the for...
We propose a new preferential attachment–based network growth model in order to explain two properti...
There is a growing interest in the study of graphs that evolve over time. Communication networks, pe...