2011/10/31 by Daniel Grady, Christian Thiemann, Dirk Brockmann · 1 citation
Mathematics · Physics and Astronomy · Psychology · #Artificial intelligence #Centrality #Complex Network Analysis Techniques #Complex network #Computer science #Data mining #Data science #Evolving networks #Machine learning #Mathematics #Mental Health Research Topics #Network theory #Opinion Dynamics and Social Influence #Range (aeronautics) #Salience (neuroscience) #Salient #Theoretical computer science #physics.soc-ph
paper · pdf · doi:10.1038/ncomms1847
Nature Communications 3 (2012)
openalex publication_date 2012/05/29 · arxiv created 2012/05/31 · arxiv updated 2012/06/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.