Performance analysis of an influence-model-based graph partitioning algorithm

Recently, a stochastic automaton known as the influence model was advanced as a tool for flexible and distributed graph partitioning, which can find optimal solutions to numerous hard partitioning problems in an almost-sure sense. Here, we provide a performance analysis of the influence model-based partitioner, for the hard problem of m-way partitioning with reference vertices. Specifically, we show that the influence model algorithm finds the optimal partition quickly with high probability, whenever the optimal cut-set is sufficiently weak compared to other cuts in the graph.