Abstract: A mimicking network for a k-terminal network, N, is one whose realizable external flows are
the same as those of N. Let S.k/ denote the minimum size of a mimicking network for a k-terminal network.
In this paper we give new constructions of mimicking networks and prove the following results (the values
in brackets are the previously best known results): S.4/ D 5 [216], S.5/ D 6 [232]. For bounded treewidth
networks we show S.k/ D O.k/ [22k ], and for outerplanar networks we show S.k/ · 10k ¡ 6 [k22kC2].

Abstract: We consider the problem of preprocessing an n-vertex digraph with real edge weights so that
subsequent queries for the shortest path or distance between any two vertices can be efficiently answered. We
give algorithms that depend on the treewidth of the input graph. When the treewidth is a constant, our algorithms
can answer distance queries in O ({\'a}(n) ) time after O.n/ preprocessing. This improves upon previously known
results for the same problem.We also give a dynamic algorithm which, after a change in an edge weight, updates
the data structure in time O.n¯ /, for any constant 0 < ¯ < 1. Furthermore, an algorithm of independent interest
is given: computing a shortest path tree, or finding a negative cycle in linear time.

Abstract: We consider the problem of preprocessing an n-vertex digraph with real edge weights so that subsequent queries for the shortest path or distance between any two vertices can be efficiently answered. We give parallel algorithms for the EREW PRAM model of computation that depend on the treewidth of the input graph. When the treewidth is a constant, our algorithms can answer distance queries in O({\'a}(n)) time using a single processor, after a preprocessing of O(log2n) time and O(n) work, where {\'a}(n) is the inverse of Ackermann's function. The class of constant treewidth graphs contains outerplanar graphs and series-parallel graphs, among others. To the best of our knowledge, these are the first parallel algorithms which achieve these bounds for any class of graphs except trees. We also give a dynamic algorithm which, after a change in an edge weight, updates our data structures in O(log n) time using O(n{\^a}) work, for any constant 0 < {\^a} < 1. Moreover, we give an algorithm of independent interest: computing a shortest path tree, or finding a negative cycle in O(log2n) time using O(n) work.