Fibonacci heaps and their uses in improved network optimization algorithms
Journal of the ACM (JACM)
Space/time trade-offs in hash coding with allowable errors
Communications of the ACM
Introduction to algorithms
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
Dynamical Processes on Complex Networks
Dynamical Processes on Complex Networks
Second order centrality: Distributed assessment of nodes criticity in complex networks
Computer Communications
K-path centrality: a new centrality measure in social networks
Proceedings of the 4th Workshop on Social Network Systems
Editorial: Complex dynamic networks: Tools and methods
Computer Networks: The International Journal of Computer and Telecommunications Networking
Distributed assessment of the closeness centrality ranking in complex networks
Proceedings of the Fourth Annual Workshop on Simplifying Complex Networks for Practitioners
k-Centralities: local approximations of global measures based on shortest paths
Proceedings of the 21st international conference companion on World Wide Web
Online estimating the k central nodes of a network
NSW '11 Proceedings of the 2011 IEEE Network Science Workshop
Optimal distributed all pairs shortest paths and applications
PODC '12 Proceedings of the 2012 ACM symposium on Principles of distributed computing
Don't count the number of friends when you are spreading information in social networks
Proceedings of the 8th International Conference on Ubiquitous Information Management and Communication
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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 limited neighborhood around each node, thus not requiring full knowledge of the network topology. We indicate 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 by the entity responsible for calculating the centrality. 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 to the analysis of large complex networks, as indicated in our experimental evaluation using both synthetically generated networks and real-world network traces of different kinds and scales.