Recursive traffic percolation on urban transportation systems.

This paper proposes a recursive traffic percolation framework to capture the dynamics of cascading failures and analyze potential overloaded bottlenecks. In particular, compared to current work, the influence of external flow is considered, providing a new perspective for the study of regional commuting. Finally, we present an empirical study to verify the accuracy and effectiveness of our framework. Further analysis indicates that external flows from different regions affect the network. Our work requires only primary data and verifies the improvement of the functional network.

Optimal transport in time-varying small-world networks.

The time-order of interactions, which is regulated by some intrinsic activity, surely plays a crucial role regarding the transport efficiency of transportation systems. Here we study the optimal transport structure by measure of the length of time-respecting paths. Our network is built from a two-dimensional regular lattice, and long-range connections are allocated with probability P(ij)∼r(ij)(-α), where r(ij) is the Manhattan distance. By assigning each shortcut an activity rate subjected to its geometric distance τ(ij)∼r(ij)(-C), long-range links become active intermittently, leading to the time-varying dynamics. We show that for 0

Origin of Gibrat law in Internet: asymmetric distribution of the correlation.

Although Gibrat's law and its generalized versions have been widely used, the organizing principle behind its phenomenological theory has been poorly studied for network-structured systems. More important, its fluctuation behavior, which contradicts the prediction of the preferential attachment (PA), indicates a nontrivial mechanism that goes beyond our present knowledge based on the traditional mean-field approach. Here, we take advantage of the rich data of the Internet and aim to identify the origin of Gibrat's law by studying the empirical fluctuation behavior. We show how the correlation between the fluctuations of the node degree increment affects the dynamics of the network. Specifically, if the distribution of the correlation is symmetric, the network evolves as the classical PA, while if such symmetry breaks, the fluctuation becomes macroscopically positively correlated and contributes to the emergence of Gibrat's law. These results indicate a local collective increase in the actual network evolution, which provides a new paradigm and understanding of the related microcosmic dynamics.