Beyond ranking nodes: Predicting epidemic outbreak sizes by network centralities

Abstract

Identifying important nodes for disease spreading is a central topic in network epidemiology. We investigate how well the position of a node, characterized by standard network measures, can predict its epidemiological importance in any graph of a given number of nodes. This is in contrast to other studies that deal with the easier prediction problem of ranking nodes by their epidemic importance in given graphs. As a benchmark for epidemic importance, we calculate the exact expected outbreak size given a node as the source. We study exhaustively all graphs of a given size, so do not restrict ourselves to certain generative models for graphs, nor to graph data sets. Due to the large number of possible nonisomorphic graphs of a fixed size, we are limited to 10-node graphs. We find that combinations of two or more centralities are predictive ($R^2$ scores of 0.91 or higher) even for the most difficult parameter values of the epidemic simulation. Typically, these successful combinations include one normalized spectral centralities (such as PageRank or Katz centrality) and one measure that is sensitive to the number of edges in the graph.

References in corpus (18)

• Scikit-learn: Machine Learning in Python
• Community detection in graphs
• Epidemic processes in complex networks
• Identification of influential spreaders in complex networks
• Mean-field theory for scale-free random networks
• Prediction and predictability of global epidemics: the role of the airline transportation network
• Vital nodes identification in complex networks
• Statistical physics of vaccination
• Social Network Sensors for Early Detection of Contagious Outbreaks
• Can co-location be used as a proxy for face-to-face contacts?
• Dynamical Patterns of Cattle Trade Movements
• Efficient local strategies for vaccination and network attack
• Identifying influential spreaders and efficiently estimating infection numbers in epidemic models: a walk counting approach
• Three faces of node importance in network epidemiology: Exact results for small graphs
• Fundamental difference between superblockers and superspreaders in networks
• Epidemic centrality - is there an underestimated epidemic impact of network peripheral nodes?
• Estimating network degree distributions under sampling: An inverse problem, with applications to monitoring social media networks
• Sampling on networks: estimating eigenvector centrality on incomplete graphs

Cited by in corpus (6)

• Top influencers can be identified universally by combining classical centralities
• Ranking influential nodes in networks from partial information
• Optimal test-kit based intervention strategy of epidemic spreading in heterogeneous complex networks
• Infection patterns in simple and complex contagion processes on networks
• Rank the spreading influence of nodes using dynamic Markov process
• Group centrality in optimal and suboptimal vaccination for epidemic models in contact networks