We analyze the class of networks characterized by modular structure where a sequence of l Erdos-Renyi random networks of size N >> 1 with random average degrees is joined by links whose structure must remain immaterial. We find that traceroutes spanning the entire macronetwork exhibit scaling degree distributions P(k) similar to k(-gamma), where gamma depends on how the degrees of the joined clusters are distributed. We thus suggest that yet another mechanism for the dynamic origin of arbitrary power-law degree distributions observed in natural and artificial networks, many of which belong to the range 2 <= gamma <= 3, may be found in random processes on modular networks
We introduce a broad class of multi-hooking networks, wherein multiple copies of a seed are hooked a...
Community structures have been identified in various complex real-world networks, for example, commu...
We show how scale-free degree distributions can emerge naturally from growing networks by using rand...
We analyze the class of networks characterized by modular structure where a sequence of l Erdos-Reny...
Background: Much work in systems biology, but also in the analysis of social network and communicati...
As for many complex systems, network structures are important as their backbone. From research on dy...
We study a scaling property of the number Mh(N) of loops of size h in complex networks with respect ...
<p>The network size is 900. The parameter α is 1.0, 0.7, 0.3 and 0.0 respectively. α = 1 corresponds...
We survey the recent work on phase transition and distances in various random graph models with gene...
<p>Right column shows illustrations of prototypical networks: the (ring) lattice small-world, the cl...
Networks with bimodal degree distribution are most robust to targeted and random attacks. We present...
Complex networks describe a variety of systems found in nature and society. Traditionally these syst...
Random networks with power-law distribution of degrees of the nodes have been studied quite extensiv...
We propose a simple mechanism for generating scale-free networks with degree exponent γ = 3, where t...
We study a simple model of dynamic networks, characterized by a set preferred degree, κ. Each node w...
We introduce a broad class of multi-hooking networks, wherein multiple copies of a seed are hooked a...
Community structures have been identified in various complex real-world networks, for example, commu...
We show how scale-free degree distributions can emerge naturally from growing networks by using rand...
We analyze the class of networks characterized by modular structure where a sequence of l Erdos-Reny...
Background: Much work in systems biology, but also in the analysis of social network and communicati...
As for many complex systems, network structures are important as their backbone. From research on dy...
We study a scaling property of the number Mh(N) of loops of size h in complex networks with respect ...
<p>The network size is 900. The parameter α is 1.0, 0.7, 0.3 and 0.0 respectively. α = 1 corresponds...
We survey the recent work on phase transition and distances in various random graph models with gene...
<p>Right column shows illustrations of prototypical networks: the (ring) lattice small-world, the cl...
Networks with bimodal degree distribution are most robust to targeted and random attacks. We present...
Complex networks describe a variety of systems found in nature and society. Traditionally these syst...
Random networks with power-law distribution of degrees of the nodes have been studied quite extensiv...
We propose a simple mechanism for generating scale-free networks with degree exponent γ = 3, where t...
We study a simple model of dynamic networks, characterized by a set preferred degree, κ. Each node w...
We introduce a broad class of multi-hooking networks, wherein multiple copies of a seed are hooked a...
Community structures have been identified in various complex real-world networks, for example, commu...
We show how scale-free degree distributions can emerge naturally from growing networks by using rand...