论文标题
时间步行中心：在不断发展的网络中排名节点
Temporal Walk Centrality: Ranking Nodes in Evolving Networks
论文作者
论文摘要
我们提出了时间步行中心性，该中心性通过测量其在时间网络中获取和分配信息的能力来量化节点的重要性。与广泛使用的中间性相反，我们假设信息不一定在最短路径上传播，而是在满足网络时间约束的时间随机步行上。我们表明，时间步行中心性可以识别出在传播过程中起着核心作用的节点，而相关概念与其他常见的静态和时间中心性测量可能无法检测到的中心作用。我们提出了具有不同运行时间的精确和近似算法，具体取决于我们新的中心度度量的时间网络的属性和参数。技术贡献是一种通用方法，可以提升现有的代数方法，以计算静态网络中的步行到时间网络。我们在现实世界中时间网络上的实验显示了算法的效率和准确性。最后，我们证明，暂时步行中心的排名通常与其他最先进的临时核心方面有很大差异。
We propose the Temporal Walk Centrality, which quantifies the importance of a node by measuring its ability to obtain and distribute information in a temporal network. In contrast to the widely-used betweenness centrality, we assume that information does not necessarily spread on shortest paths but on temporal random walks that satisfy the time constraints of the network. We show that temporal walk centrality can identify nodes playing central roles in dissemination processes that might not be detected by related betweenness concepts and other common static and temporal centrality measures. We propose exact and approximation algorithms with different running times depending on the properties of the temporal network and parameters of our new centrality measure. A technical contribution is a general approach to lift existing algebraic methods for counting walks in static networks to temporal networks. Our experiments on real-world temporal networks show the efficiency and accuracy of our algorithms. Finally, we demonstrate that the rankings by temporal walk centrality often differ significantly from those of other state-of-the-art temporal centralities.
