Search in weighted complex networks

Abstract

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.

14 pages, 5 figures, 4 tables, minor changes, added a reference

References in corpus (6)

• The architecture of complex weighted networks
• The worldwide air transportation network: Anomalous centrality, community structure, and cities' global roles
• Structural Vulnerability of the North American Power Grid
• Global organization of metabolic fluxes in the bacterium, Escherichia coli
• Modeling the evolution of weighted networks
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Cited by in corpus (10)

• Epidemic Spreading in Weighted Networks: An Edge-Based Mean-Field Solution
• Mixing navigation on networks
• Diffusive capture processes for information search
• Optimal transport on supply-demand networks
• Topology and energy transport in networks of interacting photosynthetic complexes
• A novel approach to study realistic navigations on networks
• Funnelling effect in networks
• Realistic searches on stretched exponential networks
• An egonet-based approach to effective weighted network comparison
• Statistical analysis of weighted networks