Record matching with non-key attribute values

The advanced development of various technologies on social network, e-commerce and online education has contributed to an increasing amount of large-scale network data. Among all sorts of network analysis tasks, one basic task is to search important nodes in a network. Closeness centrality is one of the popular metrics which measure the importance of a node in a network. Based on the closeness centrality, the basic task is called top-k closeness centrality search. However, the existing exact approaches cannot process large-scale networks because of their polynomial time complexity. Recently, some approximation algorithms are proposed, which achieve high performance by sacrificing the precision of results. But according to our study, we find that the loss of the precision of results is too much. To improve the precision of results while maintaining the high performance, in this paper, we propose a Sketch-based approximation algorithm for fast searching top-k closeness centrality in a large-scale network. The new algorithm is developed based on a new computation method which calculates the centrality by estimating the number of nodes within a certain distance by a data structure called FM-Sketch. The new algorithm has time complexity O(m tDmax), where t is a constant, Dmax is the diameter of a network and m is the number of edges in a network. With the small-world phenomenon assumption, the Sketch-based algorithm is a linear algorithm. Finally, we compare our Sketch-based algorithm with the state-of-the-art exact and approximation algorithms through extensive experiments. The results demonstrate the advantages of the new solution.