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The Optimization Reachability Query of Large Scale Multi-Attribute Constraints Directed Graph

Kehong Zhang, Keqiu Li
School of Computer Science and Technology, Dalian University of Technology, Dalian, China

Computer Systems Science and Engineering 2018, 33(2), 71-85.


Today, many applications such as social network and biological network develop rapidly,the graph data will be expanded constantly on a large scale. Some classic methods can not effectively solve this scale of the graph data. In the reachability query, many technologies such as N-Hop, tree, interval labels, uncertain graph processing are emerging, they also solve a lot of questions about reachability query of graph. But, these methods have not put forward the effective solution for the new issues of the multiattribute constraints reachability on directed graph. In this paper, TCRQDG algorithm effectively solves this new problem. Firstly it optimizes the multiattribute constraints with decision making technology; secondly the algorithm achieves fast and accurate query by integrating with the Create virtual vertex expand, conditions filtering, cycles contraction, interval label and other technology. TCRQDG algorithm can not only effectively solve the new problem, but also provide technical support for multiple constraints optimization decisions of network transmission, transport and logistics, software testing and other applications.


Multiattribute constraints; reachability; directed graph;interval labels;contraction

Cite This Article

K. Zhang and K. Li, "The optimization reachability query of large scale multi-attribute constraints directed graph," Computer Systems Science and Engineering, vol. 33, no.2, pp. 71–85, 2018.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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