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New Algorithms for Counting Temporal Graph Pattern

1
College of computer, National University of Defence Technology, Changsha 410073, China
2
College of computer, Beijing University of Technology, Beijing 100000, China
*
Author to whom correspondence should be addressed.
Symmetry 2019, 11(10), 1188; https://doi.org/10.3390/sym11101188
Received: 25 August 2019 / Revised: 16 September 2019 / Accepted: 17 September 2019 / Published: 20 September 2019
Temporal networks can describe multiple types of complex systems with temporal information in the real world. As an effective method for analyzing such network, temporal graph pattern (TGP) counting has received extensive attention and has been applied in diverse domains. In this paper, we study the problem of counting the TGP in the temporal network. Then, an exact algorithm is proposed based on the time first search (TFS) algorithm. This algorithm can reduce the intermediate results generated in the graph isomorphism and has high computational efficiency. To further improve the algorithm performance, we design an estimation algorithm by applying the edge sampling strategy to the exact algorithm. Finally, we evaluate the performances of the two algorithms by counting both the symmetric and asymmetric TGP. Extensive experiments on real datasets demonstrated that the exact algorithm is faster than the existing algorithm and the estimation algorithm can greatly reduce the running time while guaranteeing the accuracy. View Full-Text
Keywords: temporal network; temporal graph pattern (TGP); TGP counting; edge sampling; TFS temporal network; temporal graph pattern (TGP); TGP counting; edge sampling; TFS
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Sun, X.; Tan, Y.; Wu, Q.; Wang, J.; Shen, C. New Algorithms for Counting Temporal Graph Pattern. Symmetry 2019, 11, 1188.

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