Abstract
Several important applications related to complex network analysis require finding small vertex covers in massive sparse graphs. To fulfill this task, this paper proposes a general algorithm framework named PEAF, which includes preprocessing stage, solving stage, and inverse-processing stage. Based on PEAF, a minimum vertex cover (MinVC) solver PEAVC is developed, which uses PreP to reduce the graph, BGVC to obtain a vertex cover of bipartite-graph components, FastVC2 to solve the connected components left, and Inv_PreP to get a vertex cover of the original problem. Computational experiments on 90 massive REAL-WORLD benchmark graphs indicate that PreP can reduce the vertex number by 83.25% on average, which is superior to other graph reduction methods. PEAVC performs remarkably well by discovering 5 best-known results (new upper bounds) never reported in the literature, match the best known results for 63 other instances and obtain exact MinVCs for 55 instances. Experiments also show that PEAVC has an extremely high performance.
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