Graphs over time: densification laws, shrinking diameters and possible explanations
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Measurement and analysis of online social networks
Proceedings of the 7th ACM SIGCOMM conference on Internet measurement
Second order centrality: Distributed assessment of nodes criticity in complex networks
Computer Communications
Centrality prediction in dynamic human contact networks
Computer Networks: The International Journal of Computer and Telecommunications Networking
Online estimating the k central nodes of a network
NSW '11 Proceedings of the 2011 IEEE Network Science Workshop
Distributed Assessment of Network Centralities in Complex Social Networks
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Fast centrality-driven diffusion in dynamic networks
Proceedings of the 22nd international conference on World Wide Web companion
DACCER: Distributed Assessment of the Closeness CEntrality Ranking in complex networks
Computer Networks: The International Journal of Computer and Telecommunications Networking
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We propose a method for the Distributed Assessment of the Closeness CEntrality Ranking (DACCER) in complex networks. DACCER computes centrality based only on localized information restricted to a given neighborhood around each node, thus not requiring full knowledge of the network topology. We show that the node centrality ranking computed by DACCER is highly correlated with the node ranking based on the traditional closeness centrality, which requires high computational costs and full knowledge of the network topology. This outcome is quite useful given the vast potential applicability of closeness centrality, which is seldom applied to large-scale networks due to its high computational costs. Results indicate that DACCER is simple, yet efficient, in assessing node centrality while allowing a distributed implementation that contributes to its performance. This also contributes to the practical applicability of DACCER in the analysis of large-scale complex networks, as we show using in our experimental evaluation both synthetically generated networks and traces of real-world networks of different kinds and scales.