用Potts方法检测稠密加权网络中的模块
Detecting modules in dense weighted networks with the Potts method
摘要：
我们解决了在密集加权网络中的多分辨率模块检测问题，其中模块结构被编码在权重中而不是拓扑中。我们讨论了一个加权版本的q-状态Potts方法，这是最初介绍的Reichardt和Bornholdt。这种加权方法可以直接应用于稠密网络。我们讨论了依赖于其调整参数和网络属性的方法的分辨率，使用稀疏和密集的加权网络与内置模块作为例子的情况下。最后，我们将该方法应用于股票价格相关性的数据，并表明所得到的模块对应于这种相关性网络的已知结构特性。
英文摘要：
We address the problem of multiresolution module detection in dense weighted networks, where the modular structure is encoded in the weights rather than topology. We discuss a weighted version of the q-state Potts method, which was originally introduced by Reichardt and Bornholdt. This weighted method can be directly applied to dense networks. We discuss the dependence of the resolution of the method on its tuning parameter and network properties, using sparse and dense weighted networks with built-in modules as example cases. Finally, we apply the method to data on stock price correlations, and show that the resulting modules correspond well to known structural properties of this correlation network.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
