Du, Phuong Hanh and Nguyen, Hai Chau and Nguyen, Kim Khoa and Nguyen, Ngoc Hoa (2018) An Efficient Parallel Algorithm for Computing the Closeness Centrality in Social Network. In: 2018 the 9th International Symposium on Information and Communication Technology (SoICT), 6-7 December 2018, Da Nang City, Viet Nam.
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Abstract
Closeness centrality is an substantial metric used in large-scale network analysis, in particular social networks. Determining closeness centrality from a vertex to all other vertices in the graph is a high complexity problem. Prior work has a strong focuses on the algorithmic aspect of the problem, and little attention has been paid to the definition of the data structure supporting the implementation of the algorithm. Thus, we present in this paper an efficient algorithm to compute the closeness centrality of all nodes in a social network. Our algorithm is based on (i) an appropriate data structure for increasing the cache hit rate, and then reducing amount of time accessing the main memory for the graph data, and (ii) an efficient and parallel complete BFS search to reduce the execution time. We tested performance of our algorithm, namely BigGraph, with five different real-world social networks and compare the performance to that of current approaches including TeexGraph and NetworKit. Experiment results show that BigGraph is faster than TeexGraph and NetworKit 1.27-2.12 and 14.78-68.21 times, respectively.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Subjects: | Information Technology (IT) |
| Divisions: | Faculty of Information Technology (FIT) |
| Depositing User: | Hải Châu Nguyễn |
| Date Deposited: | 04 Jun 2019 02:48 |
| Last Modified: | 04 Jun 2019 02:48 |
| URI: | http://eprints.uet.vnu.edu.vn/eprints/id/eprint/3479 |
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- An Efficient Parallel Algorithm for Computing the Closeness Centrality in Social Network. (deposited 04 Jun 2019 02:48) [Currently Displayed]
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