Syntactic clustering of the Web
Selected papers from the sixth international conference on World Wide Web
Analysis of SIGMOD's co-authorship graph
ACM SIGMOD Record
Computing the shortest path: A search meets graph theory
SODA '05 Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete algorithms
On six degrees of separation in DBLP-DB and more
ACM SIGMOD Record
Using structure indices for efficient approximation of network properties
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Graph evolution: Densification and shrinking diameters
ACM Transactions on Knowledge Discovery from Data (TKDD)
Fast shortest path distance estimation in large networks
Proceedings of the 18th ACM conference on Information and knowledge management
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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.