Confidence bounds for threshold similarity graph in random variable network

Abstract

Problem of uncertainty of graph structure identification in random variable network is considered. An approach for the construction of upper and lower confidence bounds for graph structures is developed. This approach is applied for the construction of upper and lower confidence bounds for the threshold similarity graph. The stability of confidence bounds and gaps between upper and lower confidence bounds are investigated. Theoretical results are illustrated by numerical experiments.