Fast approximation of centrality
SODA '01 Proceedings of the twelfth annual ACM-SIAM symposium on Discrete algorithms
Communications of the ACM
Introduction to algorithms
Communicating Centrality in Policy Network Drawings
IEEE Transactions on Visualization and Computer Graphics
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
Experimental analysis of dynamic all pairs shortest path algorithms
ACM Transactions on Algorithms (TALG)
Ranking of Closeness Centrality for Large-Scale Social Networks
FAW '08 Proceedings of the 2nd annual international workshop on Frontiers in Algorithmics
Extensions of closeness centrality?
Proceedings of the 49th Annual Southeast Regional Conference
Incremental algorithm for updating betweenness centrality in dynamically growing networks
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
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Automation of data collection using online resources has led to significant changes in traditional practices of social network analysis. Social network analysis has been an active research field for many decades; however, most of the early work employed very small datasets. In this paper, a number of issues with traditional practices of social network analysis in the context of dynamic, large-scale social networks are pointed out. Given the continuously evolving nature of modern online social networking, we postulate that social network analysis solutions based on incremental algorithms will become more important to address high computation times for large, streaming, over-time datasets. Incremental algorithms can benefit from early pruning by updating the affected parts only when an incremental update is made in the network. This paper provides an example of this case by demonstrating the design of an incremental closeness centrality algorithm that supports efficient computation of all-pairs of shortest paths and closeness centrality in dynamic social networks that are continuously updated by addition, removal, and modification of nodes and edges. Our results obtained on various synthetic and real-life datasets provide significant speedups over the most commonly used method of computing closeness centrality, suggesting that incremental algorithm design is a fruitful research area for social network analysts.