Analysing the sensitivity of nestedness detection methods

Many bipartite and unipartite real-world networks display a nested structure. Examples pervade different disciplines: biological ecosystems (e.g. mutualistic networks), economic networks (e.g. manufactures and contractors networks) to financial networks (e.g. bank lending networks), etc. A nested network has a topology such that a vertex’s neighbourhood contains the neighbourhood of vertices of lower degree; thus –upon vertex reordering– the adjacency matrix is step-wise. Despite its strict mathematical definition and the interest triggered by their common occurrence, it is not easy to measure the extent of nested graphs unequivocally. Among others, there exist three methods for detection and quantification of nestedness that are widely used: BINMATNEST, NODF, and fitness-complexity metric (FCM). However, these methods fail in assessing the existence of nestedness for graphs of low (NODF) and high (NODF, BINMATNEST) network density. Another common shortcoming of these approaches is the underlying assumption that all vertices belong to a nested component. However, many real-world networks have solely a sub-component (i.e. a subset of its vertices) that is nested. Thus, unveiling which vertices pertain to the nested component is an important research question, unaddressed by the methods available so far. In this contribution, we study in detail the algorithm Nestedness detection based on Local Neighbourhood (NESTLON). This algorithm resorts solely on local information and detects nestedness on a broad range of nested graphs independently of their nature and density. Further, we introduce a benchmark model that allows us to tune the degree of nestedness in a controlled manner and study the performance of different algorithms. Our results show that NESTLON outperforms both BINMATNEST and NODF.