DANCE: A Framework for the Distributed Assessment of Network Centralities

TL;DR

DANCE is a flexible framework enabling the distributed, parallel assessment of various localized network centralities, facilitating large-scale complex network analysis across multiple scientific disciplines.

Contribution

It introduces a unified environment for applying different localized centrality measures in a distributed manner, adaptable to various applications and computing architectures.

Findings

Parallel implementation of DANCE improves scalability

Applicable to large-scale networks in multiple fields

Provides a web portal for easy access and use

Abstract

The analysis of large-scale complex networks is a major challenge in the Big Data domain. Given the large-scale of the complex networks researchers commonly deal with nowadays, the use of localized information (i.e. restricted to a limited neighborhood around each node of the network) for centrality-based analysis is gaining momentum in the recent literature. In this context, we propose a framework for the Distributed Assessment of Network Centralities (DANCE) in complex networks. DANCE offers a single environment that allows the use of different localized centrality proposals, which can be tailored to specific applications. This environment can be thus useful given the vast potential applicability of centrality-based analysis on large-scale complex networks found in different areas, such as Biology, Physics, Sociology, or Computer Science. Since the localized centrality proposals DANCE implements employ only localized information, DANCE can easily benefit from parallel processing environments and run on different computing architectures. To illustrate this, we present a parallel implementation of DANCE and show how it can be applied to the analysis of large-scale complex networks using different kinds of network centralities. This implementation is made available to complex network researchers and practitioners interested in using it through a scientific web portal.