Complex networks are widely used to model the structure of many complex systems in
nature and society. Recently, fractal and self-similarity of complex networks have attracted …

Recently, self-similarity of complex networks have attracted much attention. Fractal
dimension of complex network is an open issue. Hub repulsion plays an important role in …

The fractal and self-similarity properties are revealed in many complex networks. The
classical information dimension is an important method to study fractal and self-similarity …

Self-similarity of complex networks has been discovered and attracted much attention.
However, the self-similarity of complex networks was measured by the classical distance of …

It is shown that many real complex networks share distinctive features, such as the small-
world effect and the heterogeneous property of connectivity of vertices, which are different …

The fractal and self-similarity properties are investigated in many real complex networks.
The volume dimension method is an effective tool to measure the fractal property of complex …

It has been shown that many complex networks shared distinctive features, which differ in
many ways from the random and the regular networks. Although these features capture …

Revealing the properties of the core-periphery structure of complex networks can help us to
deeply understand their organized principle and function. Studies in recent years have …

A concept of higher order neighborhood in complex networks, introduced previously [Phys.
Rev. E 73, 046101 (2006)], is systematically explored to investigate larger scale structures in …

The cluster-growing method has been widely used to measure the fractal dimension of
complex networks. In this method, a seed node is chosen at random and the number of …