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20032026
most citedDeep learning in remote sensing: a review

3.2k citations

Showing 2007 · physics.soc-phShow all

6 papers · 2 filters

physics.soc-ph2007★ 60 cited

Symmetry based Structure Entropy of Complex Networks

Yanghua Xiao, Wentao Wu, Hui Wang +2

Precisely quantifying the heterogeneity or disorder of a network system is very important and desired in studies of behavior and function of the network system. Although many degre…

physics.soc-ph2007★ 46 cited

Evolving small-world scale-free networks consist of cliques

Zhongzhi Zhang, Shuigeng Zhou, Lichao Chen

We present a family of scale-free network model consisting of cliques, which is established by a simple recursive algorithm. We investigate the networks both analytically and numer…

physics.soc-ph2007★ 52 cited

Emergence of Symmetry in Complex Networks

Yanghua Xiao, Momiao Xiong, Wei Wang +1

Many real networks have been found to have a rich degree of symmetry, which is a very important structural property of complex network, yet has been rarely studied so far. And wher…

physics.soc-ph2007★ 1 cited

A Synthetical Weights' Dynamic Mechanism for Weighted Networks

Lujun Fang, Zhongzhi Zhang, Shuigeng Zhou +1

We propose a synthetical weights' dynamic mechanism for weighted networks which takes into account the influences of strengths of nodes, weights of links and incoming new vertices.…

physics.soc-ph2007★ 90 cited

Maximal planar scale-free Sierpinski networks with small-world effect and power-law strength-degree correlation

Zhongzhi Zhang, Shuigeng Zhou, Lujun Fang +2

Many real networks share three generic properties: they are scale-free, display a small-world effect, and show a power-law strength-degree correlation. In this paper, we propose a…

physics.soc-ph2007★ 23 cited

Effects of accelerating growth on the evolution of weighted complex networks

Zhongzhi Zhang, Lujun Fang, Shuigeng Zhou +1

Many real systems possess accelerating statistics where the total number of edges grows faster than the network size. In this paper, we propose a simple weighted network model with…