Qi Liang
Qi Liang
*This is an algorithmically generated author record based on publicly available information.
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Qi Liang has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Dian Ouyang
Guangzhou University, Guangzhou, Guangdong, CHINA
1 shared publication - Yikun Wang
Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 511400, China
1 shared publication - Zhuo Huang
Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 511400, China
1 shared publication - Zhuoran Wang
Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, China
1 shared publication
Latest Publications
- Open Access
- 6 May 2023
- Published by MDPI AG in Mathematics
- Vol. 11 (9) , 2200
Efficient and Effective Directed Minimum Spanning Tree Queries
Computing directed Minimum Spanning Tree () is a fundamental problem in graph theory. It is applied in a wide spectrum of fields from computer network and communication protocol design to revenue maximization in social networks and syntactic parsing in Natural Language Processing. State-of-the-art solutions are online algorithms that compute for a given graph and a root. For multi-query requirements, the online algorithm is inefficient. To overcome the drawbacks, in this paper, we propose an indexed approach that reuses the computation result to facilitate single and batch queries. We store all the potential edges of in a hierarchical tree in space complexity. Furthermore, we answer the query of any root in time complexity. Experimental results demonstrate that our approach can achieve a speedup of 2–3 orders of magnitude in query processing compared to the state-of-the-art while consuming O(n) index size.- 6 May 2023
- Published by MDPI AG in Mathematics
- Vol. 11 (9) , 2200
Open AccessEfficient and Effective Directed Minimum Spanning Tree Queries
Computing directed Minimum Spanning Tree () is a fundamental problem in graph theory. It is applied in a wide spectrum of fields from computer network and communication protocol design to revenue maximization in social networks and syntactic parsing in Natural Language Processing. State-of-the-art solutions are online algorithms that compute for a given graph and a root. For multi-query requirements, the online algorithm is inefficient. To overcome the drawbacks, in this paper, we propose an indexed approach that reuses the computation result to facilitate single and batch queries. We store all the potential edges of in a hierarchical tree in space complexity. Furthermore, we answer the query of any root in time complexity. Experimental results demonstrate that our approach can achieve a speedup of 2–3 orders of magnitude in query processing compared to the state-of-the-art while consuming O(n) index size.