This paper explores the relationship between two ideas in network information theory: edge removal and strong converses. Edge removal properties state that if an edge of small capacity is removed from a network, the capacity region does not change too much. Strong converses state that, for rates outside the capacity region, the probability of error converges to 1. Various notions of edge removal and strong converse are defined, depending on how edge capacity and residual error probability scale with blocklength, and relations between them are proved. In particular, each class of strong converse implies a specific class of edge removal. The opposite directions are proved for deterministic networks. Furthermore, a technique based on a novel causal version of the blowing-up lemma is used to prove that for discrete memoryless stationary networks, the weak edge removal property---that the capacity region changes continuously as the capacity of an edge vanishes---is equivalent to the exponen 查看全文>>