  We study trade-offs presented by local search algorithms in complex networks
which are heterogeneous in edge weights and node degree. We show that search
based on a network measure, local betweenness centrality (LBC), utilizes the
heterogeneity of both node degrees and edge weights to perform the best in
scale-free weighted networks. The search based on LBC is universal and performs
well in a large class of complex networks.
