Abstract
The goal of this work is to identify the nodes that have the smallest sum of distances to other nodes (the lowest closeness centrality nodes) in graphs that evolve over time. Previous approaches to this problem find the lowest centrality nodes efficiently at the expense of exactness. The main motivation of this paper is to answer, in the affirmative, the question, ‘Is it possible to improve the search time without sacrificing the exactness?’. Our solution is Sniper, a fast search method for time-evolving graphs. Sniper is based on two ideas: (1) It computes approximate centrality by reducing the original graph size while guaranteeing the answer exactness, and (2) It terminates unnecessary distance computations early when pruning unlikely nodes. The experimental results show that Sniper can find the lowest centrality nodes significantly faster than the previous approaches while it guarantees answer exactness.
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Fujiwara, Y., Onizuka, M., Kitsuregawa, M. (2011). Efficient Centrality Monitoring for Time-Evolving Graphs. In: Huang, J.Z., Cao, L., Srivastava, J. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2011. Lecture Notes in Computer Science(), vol 6635. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20847-8_4
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DOI: https://doi.org/10.1007/978-3-642-20847-8_4
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