复杂网络的距离骨干
The distance backbone of complex networks
摘要：
冗余度需要更精确的表征，因为它是多变量交互网络进化和鲁棒性的主要因素。我们调查这种相互作用的复杂性，通过推断连接传递性，包括所有可能的措施，加权图的路径长度。其结果是，在不将图分解成更小的组件的情况下，距离骨干子图足以计算所有最短路径。这对于理解真实世界网络中传播和通信现象的动态非常重要。我们正式推导出的一般方法产生了一个原则性的图形简化技术，并提供了一个更精细的特征的三角形几何的所有边缘-那些有助于最短路径和那些不，但参与其他网络现象。我们证明，在从空中交通到人脑连接体的大型网络中，距离骨干非常小，这表明网络对攻击和故障的鲁棒性似乎源于惊人的大量冗余。
英文摘要：
Redundancy needs more precise characterization as it is a major factor in the evolution and robustness of networks of multivariate interactions. We investigate the complexity of such interactions by inferring a connection transitivity that includes all possible measures of path length for weighted graphs. The result, without breaking the graph into smaller components, is a distance backbone subgraph sufficient to compute all shortest paths. This is important for understanding the dynamics of spread and communication phenomena in real-world networks. The general methodology we formally derive yields a principled graph reduction technique and provides a finer characterization of the triangular geometry of all edges -- those that contribute to shortest paths and those that do not but are involved in other network phenomena. We demonstrate that the distance backbone is very small in large networks across domains ranging from air traffic to the human brain connectome, revealing that network robustness to attacks and failures seems to stem from surprisingly vast amounts of redundancy.
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。
