Average case analysis of dynamic graph algorithms

We present a model with restricted randomness for edge updates in dynamic graph algorithms and a general technique for analyzing the expected running time of an update operation. This model is able to capture the average case in many applications, since (1) it allows restrictions on the set of edges which can be used for insertions and (2) the type (insertion or deletion) of each update operation is arbitrary, i.e., not random. We use our technique to analyze existing and new dynamic algorithms for maximum cardinality matching, minimum spanning forest, connectivity, 2-edge connectivity, k-edge connectivity, k-vertex connectivity, and bipartiteness. Given a random graph G with mo edges and n vertices and a sequence of 1 update operations such that the graph contains rni edges after operation i, the expected time for performing the updates for any 1 is O(1 logn + n xi=, l/fii) in the case of minimum spanning forests, connectivity, 2- edge connectivity, and bipartiteness. The expected time per update operation is O(n) in the case of maximum matching. For k-edge and k-vertex connectivity we also give improved bounds. Additionally we give an insertions-only algorithm for maximum cardinality matching with worst-case O(n) amortized time per insertion.