2014/05/31 by Glenn Lawyer · 193 citations
Computer Science · Engineering · Mathematics · Physics and Astronomy · Psychology · #Adjacency matrix #Centrality #Combinatorics #Complex Network Analysis Techniques #Complex network #Computer science #Degree (music) #Degree distribution #Eigenvalues and eigenvectors #Engineering #Mathematics #Mental Health Research Topics #Metric (unit) #Node (physics) #Opinion Dynamics and Social Influence #Physics #Preferential attachment #Scale-free network #The Internet #Theoretical computer science #Topology (electrical circuits) #cs.CY #cs.SI #physics.soc-ph
paper · pdf · doi:10.1038/srep08665
published in Scientific Reports 5(1), 8665 (Nature Portfolio)
arxiv created 2014/06/11 · openalex publication_date 2015/03/02 · arxiv updated 2016/05/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/31
Centrality measures such as the degree, k-shell, or eigenvalue centrality can identify a network's most influential nodes, but are rarely usefully accurate in quantifying the spreading power of the vast majority of nodes which are not highly influential. The spreading power of all network nodes is better explained by considering, from a continuous-time epidemiological perspective, the distribution of the force of infection each node generates. The resulting metric, the expected force, accurately quantifies node spreading power under all primary epidemiological models across a wide range of archetypical human contact networks. When node power is low, influence is a function of neighbor degree. As power increases, a node's own degree becomes more important. The strength of this relationship is modulated by network structure, being more pronounced in narrow, dense networks typical of social networking and weakening in broader, looser association networks such as the Internet. The expected force can be computed independently for individual nodes, making it applicable for networks whose adjacency matrix is dynamic, not well specified, or overwhelmingly large.