Robust classification of salient links in complex networks

Abstract

Complex networks in natural, social, and technological systems generically exhibit an abundance of rich information. Extracting meaningful structural features from data is one of the most challenging tasks in network theory. Many methods and concepts have been proposed to address this problem such as centrality statistics, motifs, community clusters, and backbones, but such schemes typically rely on external and arbitrary parameters. It is unknown whether generic networks permit the classification of elements without external intervention. Here we show that link salience is a robust approach to classifying network elements based on a consensus estimate of all nodes. A wide range of empirical networks exhibit a natural, network-implicit classification of links into qualitatively distinct groups, and the salient skeletons have generic statistical properties. Salience also predicts essential features of contagion phenomena on networks, and points towards a better understanding of universal features in empirical networks that are masked by their complexity.

arXiv commentNature Communications 3 (2012)

References in corpus (32)

• Emergence of scaling in random networks
• Statistical mechanics of complex networks
• The structure and function of complex networks
• Finding and evaluating community structure in networks
• Power-law distributions in empirical data
• Assortative mixing in networks
• The structure of scientific collaboration networks
• The architecture of complex weighted networks
• The large-scale organization of metabolic networks
• Analysis of weighted networks
• Spatial Networks
• The scaling laws of human travel
• Hierarchical Organization in Complex Networks
• The worldwide air transportation network: Anomalous centrality, community structure, and cities' global roles
• The Web of Human Sexual Contacts
• Prediction and predictability of global epidemics: the role of the airline transportation network
• Forecast and Control of Epidemics in a Globalized World
• The complex network of global cargo ship movements
• A tool for filtering information in complex systems
• Extracting the multiscale backbone of complex weighted networks
• Global organization of metabolic fluxes in the bacterium, Escherichia coli
• Topology of the World Trade Web
• Extracting the hierarchical organization of complex systems
• Structure and Evolution of the World Trade Network
• Classes of complex networks defined by role-to-role connectivity profiles
• Study of time-dependent CP violation in B0 -> J/Psi pi0 decays
• Fitness-dependent topological properties of the World Trade Web
• Robust Patterns in Food Web Structure
• Core-periphery organization of complex networks
• The structure of borders in a small world
• Transport in weighted networks: Partition into superhighways and roads
• Information filtering in complex weighted networks

Cited by in corpus (27)

• Structure and dynamics of molecular networks: A novel paradigm of drug discovery. A comprehensive review
• Saving Human Lives: What Complexity Science and Information Systems can Contribute
• Structure and dynamics of core-periphery networks
• Hierarchical Block Structures and High-resolution Model Selection in Large Networks
• Topological strata of weighted complex networks
• Critical links and nonlocal rerouting in complex supply networks
• The hidden geometry of weighted complex networks
• A parametric approach to information filtering in complex networks: The Pólya filter
• A new dominance concept and its application to diversity-stability analysis
• Accurate ranking of influential spreaders in networks based on dynamically asymmetric link-impact
• Betweenness centrality of teams in social networks
• An Evaluation Tool for Backbone Extraction Techniques in Weighted Complex Networks
• Robustness of skeletons and salient features in networks
• Sentiment and structure in word co-occurrence networks on Twitter
• Emergence of geometric Turing patterns in complex networks
• Percolation and the effective structure of complex networks
• Physically-interpretable classification of biological network dynamics for complex collective motions
• Extracting the signed backbone of intrinsically dense weighted networks
• Predicting the evolution of complex networks via local information
• Identification of highly susceptible individuals in complex networks
• Convex skeletons of complex networks
• Weighted H-index for identifying influential spreaders
• The Atlas for the Aspiring Network Scientist
• Extracting hierarchical backbones from bipartite networks
• Computing well-balanced spanning trees of unweighted networks
• Identifying maximal sets of significantly interacting nodes in higher-order networks
• Risk mapping novel respiratory pathogens with large-scale dynamic contact networks