arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1801.06009cond-mat.stat-mechcond-mat.dis-nn
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用光谱熵拟合网络模型的热力学

Thermodynamics of network model fitting with spectral entropies

Carlo Nicolini, Vladimir Vlasov, Angelo Bifone

摘要:

最近提出了一种受量子统计力学启发的信息论方法,作为优化网络模型并评估其与合成和真实世界网络的可能性的一种手段。重要的是,这种方法不依赖于特定的拓扑特征或网络描述符,而是利用基于熵的网络距离度量。娱乐与热力学的类比,我们提供了一个物理解释的模型超参数,并提出了分析程序的估计。这些结果使这个新颖而强大的框架能够实际应用于网络模型推理。我们在具有模块化结构的合成网络和现实世界的大脑连接网络中展示了这种方法。

英文摘要:

An information theoretic approach inspired by quantum statistical mechanics was recently proposed as a means to optimize network models and to assess their likelihood against synthetic and real-world networks. Importantly, this method does not rely on specific topological features or network descriptors, but leverages entropy-based measures of network distance. Entertaining the analogy with thermodynamics, we provide a physical interpretation of model hyperparameters and propose analytical procedures for their estimate. These results enable the practical application of this novel and powerful framework to network model inference. We demonstrate this method in synthetic networks endowed with a modular structure, and in real-world brain connectivity networks.

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。

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