2017/10/03 by Renquan Zhang, Sen Pei
Computer Science · Engineering · Mathematics · Physics and Astronomy · #Advanced Memory and Neural Computing #Algorithm #Backtracking #Complex Network Analysis Techniques #Computer science #Dynamic programming #Eigenvalues and eigenvectors #Engineering #Heuristics #Mathematical optimization #Mathematics #Maximization #Neural Networks and Reservoir Computing #Physics #Range (aeronautics) #nlin.AO #physics.soc-ph
paper · pdf · doi:10.1063/1.4997254
10 pages, 8 figures
arxiv created 2017/10/03 · openalex publication_date 2018/01/01 · arxiv updated 2018/01/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
We study the strategy to optimally maximize the dynamic range of excitable networks by removing the minimal number of links. A network of excitable elements can distinguish a broad range of stimulus intensities and has its dynamic range maximized at criticality. In this study, we formulate the activation propagation in excitable networks as a message passing process in which a critical state is reached when the largest eigenvalue of the weighted non-backtracking matrix is close to one. By considering the impact of single link removal on the largest eigenvalue, we develop an efficient algorithm that aims to identify the optimal set of links whose removal will drive the system to the critical state. Comparisons with other competing heuristics on both synthetic and real-world networks indicate that the proposed method can maximize the dynamic range by removing the smallest number of links, and at the same time maintaining the largest size of the giant connected component.