Ensemble inference of unobserved infections in networks using partial observations
Fig 7
Computational complexity of the ensemble inference algorithm.
(A). Computation time for increasing number of nodes with a fixed number of observations (No = 200). Experiments were run on ER random networks with an average degree 3. Distributions of running time were obtained from 100 runs. Boxes show the median and interquartile. Whiskers show 1.5 times the interquartile range away from the bottom or top of the boxes. The inset shows the fitting of the computation time against the number of nodes (both log-transformed). (B). Computation time for a fix number of nodes 3,000 and a fix average degree 3 with increasing number of observations. (C). Computation time for a fix number of nodes 3,000 and a fix number of observations 200 with increasing average degrees. (D). Computation time for increasing numbers of nodes with a fixed percentage of observations (15%). (E). Computation time for increasing number of ensemble members in an ER random network with 3,000 nodes, an average degree of 3, and 200 observations.