Inter-similarity between coupled networks

Recent studies have shown that a system composed from several randomly interdependent networks is extremely vulnerable to random failure. However, real interdependent networks are usually not randomly interdependent, rather a pair of dependent nodes are coupled according to some regularity which we coin inter-similarity. For example, we study a system composed from an interdependent world wide port network and a world wide airport network and show that well connected ports tend to couple with well connected airports. We introduce two quantities for measuring the level of inter-similarity between networks (i) Inter degree-degree correlation (IDDC) (ii) Inter-clustering coefficient (ICC). We then show both by simulation models and by analyzing the port-airport system that as the networks become more inter-similar the system becomes significantly more robust to random failure.Comment: 4 pages, 3 figure

Airline data for global city network research: reviewing and refining existing approaches.

Information on air passenger flows is potentially a prime data source for assessing spatial patterns in the global city network, but previous analyses have been hampered by inadequate and/or partial data. The ensuing analytical deficiencies have reduced the overall value of these analyses, and this paper examines how some of these deficiencies may be rectified. First, we review the rationale for using airline data to analyse the global city network. Second, we assess the data problems encountered in previous research. Third, we elaborate on the construction of datasets that may circumvent some of these problems. The proposed refinements include the omission of the hub function of major airports and ways to extract relevant business flows from the data