Lower and upper bounds for incremental algorithms
Lower and upper bounds for incremental algorithms
Communications of the ACM
Fully Dynamic Algorithms for Maintaining All-Pairs Shortest Paths and Transitive Closure in Digraphs
FOCS '99 Proceedings of the 40th Annual Symposium on Foundations of Computer Science
A new approach to dynamic all pairs shortest paths
Journal of the ACM (JACM)
Experimental analysis of dynamic all pairs shortest path algorithms
ACM Transactions on Algorithms (TALG)
Graph evolution: Densification and shrinking diameters
ACM Transactions on Knowledge Discovery from Data (TKDD)
GT: picking up the truth from the ground for internet traffic
ACM SIGCOMM Computer Communication Review
Centrality metric for dynamic networks
Proceedings of the Eighth Workshop on Mining and Learning with Graphs
Analysing information flows and key mediators through temporal centrality metrics
Proceedings of the 3rd Workshop on Social Network Systems
QUBE: a quick algorithm for updating betweenness centrality
Proceedings of the 21st international conference on World Wide Web
Heuristics for Speeding Up Betweenness Centrality Computation
SOCIALCOM-PASSAT '12 Proceedings of the 2012 ASE/IEEE International Conference on Social Computing and 2012 ASE/IEEE International Conference on Privacy, Security, Risk and Trust
Incremental closeness centrality for dynamically changing social networks
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
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The increasing availability of dynamically growing digital data that can be used for extracting social networks has led to an upsurge of interest in the analysis of dynamic social networks. One key aspect of social network analysis is to understand the central nodes in a network. However, dynamic calculation of centrality values for rapidly growing networks might be unfeasibly expensive, especially if it involves recalculation from scratch for each time period. This paper proposes an incremental algorithm that effectively updates betweenness centralities of nodes in dynamic social networks while avoiding re-computations by exploiting information from earlier computations. Our performance results suggest that our incremental betweenness algorithm can achieve substantial performance speedup, on the order of thousands of times, over the state of the art, including the best-performing non-incremental betweenness algorithm and a recently proposed betweenness update algorithm.