کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
973553 | 1480113 | 2016 | 16 صفحه PDF | دانلود رایگان |
• We study analytically and numerically the fitness–complexity metric (FCM) and the minimal extremal metric (MEM) for nested networks.
• For both metrics, we derive exact equations for node scores in perfectly nested matrices.
• Our analytic results explain the convergence properties of the fitness–complexity metric.
• In real data, the MEM can produce improved rankings if the input data are reliable.
Numerical analysis of data from international trade and ecological networks has shown that the non-linear fitness–complexity metric is the best candidate to rank nodes by importance in bipartite networks that exhibit a nested structure. Despite its relevance for real networks, the mathematical properties of the metric and its variants remain largely unexplored. Here, we perform an analytic and numeric study of the fitness–complexity metric and a new variant, called minimal extremal metric. We rigorously derive exact expressions for node scores for perfectly nested networks and show that these expressions explain the non-trivial convergence properties of the metrics. A comparison between the fitness–complexity metric and the minimal extremal metric on real data reveals that the latter can produce improved rankings if the input data are reliable.
Journal: Physica A: Statistical Mechanics and its Applications - Volume 460, 15 October 2016, Pages 254–269