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The analysis of large-scale complex networks is a major challenge in the BigData domain. Given the large-scale of the complex networks researchers commonlydeal with nowadays, the use of localized information (i.e. restricted to alimited neighborhood around each node of the network) for centrality-basedanalysis is gaining momentum in the recent literature. In this context, wepropose a framework for the Distributed Assessment of Network Centralities(DANCE) in complex networks. DANCE offers a single environment that allows theuse of different localized centrality proposals, which can be tailored tospecific applications. This environment can be thus useful given the vastpotential applicability of centrality-based analysis on large-scale complexnetworks found in different areas, such as Biology, Physics, Sociology, orComputer Science. Since the localized centrality proposals DANCE implementsemploy only localized information, DANCE can easily benefit from parallelprocessing environments and run on different computing architectures. Toillustrate this, we present a parallel implementation of DANCE and show how itcan be applied to the analysis of large-scale complex networks using differentkinds of network centralities. This implementation is made available to complexnetwork researchers and practitioners interested in using it through ascientific web portal.

DANCE: A Framework for the Distributed Assessment of Network Centralities