一个基于熵的、依赖于尺度的中心性
An entropy-based, scale-dependent centrality
摘要：
在这篇文章中，我们引入了一个基于熵的，尺度依赖的中心性，它被评估为连续时间随机游走在时间t的分布的香农熵。它将节点排列为时间t的函数，时间t作为一个参数，定义了网络的规模。它能够捕获众所周知的中心性，如度，特征向量和接近度，这取决于t的范围。我们比较它与广泛的类总$f$的可沟通性中心，其中卡茨中心和总的可沟通性的特殊情况。
英文摘要：
In this article we introduce an entropy-based, scale-dependent centrality that is evaluated as the Shannon entropy of the distribution at time t of a continuous-time random walk. It ranks nodes as a function of the time t, which acts as a parameter and defines the scale of the network. It is able capture well-known centralities such as degree, eigenvector and closeness depending on the range of t. We compare it with the broad class of total $f$-communicability centralities, of which both Katz centrality and total communicability are particular cases.
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。
