Localization of nonbacktracking centrality on dense subgraphs of sparse networks

The nonbacktracking matrix and the related nonbacktracking centrality (NBC) play a crucial role in models of percolation-type processes on networks, such as nonrecurrent epidemics. Here we study the localization of NBC in infinite sparse networks that contain an arbitrary finite subgraph. Assuming the local tree likeness of the enclosing network, and that branches emanating from the finite subgraph do not intersect at finite distances, we show that the largest eigenvalue of the nonbacktracking matrix of the composite network is equal to the highest of the two largest eigenvalues: that of the finite subgraph and of the enclosing network. In the localized state, when the largest eigenvalue of the subgraph is the highest of the two, we derive explicit expressions for the NBCs of nodes in the subgraph and other nodes in the network. In this state, nonbacktracking centrality is concentrated on the subgraph and its immediate neighborhood in the enclosing network. We obtain simple, exact formulas in the case where the enclosing network is uncorrelated. We find that the mean NBC decays exponentially around the finite subgraph, at a rate which is independent of the structure of the enclosing network, contrary to what was found for the localization of the principal eigenvector of the adjacency matrix. Numerical simulations confirm that our results provide good approximations even in moderately sized, loopy, real-world networks.

Automated summary

The nonbacktracking matrix and its centrality measure play a significant role in percolation-type processes on networks. This study investigates the localization of nonbacktracking centrality in infinite sparse networks containing a finite subgraph. The results show that the largest eigenvalue of the nonbacktracking matrix of the composite network is determined by the larger of the two largest eigenvalues of the subgraph and the enclosing network. In the localized state, the nonbacktracking centrality is concentrated on the subgraph and its immediate neighborhood in the enclosing network.