论文标题
近似网络对称性
Approximate Network Symmetry
论文作者
论文摘要
我们定义了一个新的网络对称性度量，该量度能够捕获网络的近似全局对称性。我们将此度量应用于从几个经典网络模型以及几个现实世界网络中采样的不同网络。我们发现，在我们研究的网络模型中，Erdös-rényi网络具有最小的对称性，并且随机几何图可能具有很高的对称性。我们发现我们的网络对称度量可以捕获网络结构的属性，并帮助我们获得对现实网络结构的见解。此外，我们的网络对称性度量能够捕获不完美的网络对称性，如果仅考虑完美的对称性，就会未发现。
We define a new measure of network symmetry that is capable of capturing approximate global symmetries of networks. We apply this measure to different networks sampled from several classic network models, as well as several real-world networks. We find that among the network models that we have examined, Erdös-Rényi networks have the least levels of symmetry, and Random Geometric Graphs are likely to have high levels of symmetry. We find that our network symmetry measure can capture properties of network structure, and help us gain insights on the structure of real-world networks. Moreover, our network symmetry measure is capable of capturing imperfect network symmetry, which would have been undetected if only perfect symmetry is considered.
