The observed architecture of ecological and socio-economic networks differssignificantly
from that of random networks. From a network science standpoint, non-random structural …

Coupled biological and chemical systems, neural networks, social interacting species, the
Internet and the World Wide Web, are only a few examples of systems composed by a large …

Many real networks are equipped with short diameters, high clustering, and power-law
degree distributions. With preferential attachment and network growth, the model by …

Many empirical networks originate from correlational data, arising in domains as diverse as
psychology, neuroscience, genomics, microbiology, finance, and climate science …

We study synthetic temporal networks whose evolution is determined by stochastically
evolving node variables-synthetic analogues of, eg, temporal proximity networks of mobile …

The scale-free (SF) structures that commonly appear in many complex networks are a hot
topic in social, biological, and information sciences. These self-organized generation …

It is important in computer science, sociology, and so on to investigate complex bipartite
graphs from a viewpoint of statistical physics. We propose a model to generate complex …

The threshold model can be used to generate random networks of arbitrary size with given
local properties such as the degree distribution, clustering, and degree correlation. We …

In this paper, the Turing instability in reaction–diffusion models defined on complex networks
is studied. Here, we focus on three types of models which generate complex networks, ie the …

Hubs, or vertices with large degrees, play massive roles in, for example, epidemic dynamics,
innovation diffusion, and synchronization on networks. However, costs of owning edges can …