2017/07/31 by Pierre-Louis Giscard, Richard C. Wilson
Computer Science · Economics, Econometrics and Finance · Environmental Science · Physics and Astronomy · #Baseline (sea) #Centrality #Complex Network Analysis Techniques #Complex Systems and Time Series Analysis #Complex network #Ecosystem dynamics and resilience #Eigenvalues and eigenvectors #Fraction (chemistry) #Measure (data warehouse) #Network science #Network theory #cs.DM #cs.SI #physics.soc-ph
paper · pdf · doi:10.1007/978-3-319-72150-7_2
published as Proceedings of Complex Networks 2017 (The Sixth International Conference on Complex Networks and Their Applications), pp 14-28
openalex publication_date 2017/11/26 · openalex created_date 2017/12/04 · arxiv created 2017/12/05 · arxiv updated 2017/12/06 · openalex updated_date 2026/08/06
Networks are versatile representations of the interactions between entities in complex systems. Cycles on such networks represent feedback processes which play a central role in system dynamics. In this work, we introduce a measure of the importance of any individual cycle, as the fraction of the total information flow of the network passing through the cycle. This measure is computationally cheap, numerically well-conditioned, induces a centrality measure on arbitrary subgraphs and reduces to the eigenvector centrality on vertices. We demonstrate that this measure accurately reflects the impact of events on strategic ensembles of economic sectors, notably in the US economy. As a second example, we show that in the protein-interaction network of the plant Arabidopsis thaliana, a model based on cycle-centrality better accounts for pathogen activity than the state-of-art one. This translates into pathogen-targeted-proteins being concentrated in a small number of triads with high cycle-centrality. Algorithms for computing the centrality of cycles and subgraphs are available for download.