Papers
6
Total Citations
109
H-Index
5
About
Dr. Jia-Bao Liu is a leading researcher in graph theory and its applications to intelligent systems, with a particular focus on metric dimension theory and network resolvability. His work addresses fundamental problems in robot navigation, facility location, and network optimization by developing mathematical frameworks for determining the minimum number of nodes needed to uniquely identify all locations in a graph. Among his most influential contributions are his studies on the partition dimension of convex polytopes (35 citations) and sharp bounds of local fractional metric dimensions in connected networks (34 citations), both published in 2020. These papers provide critical tools for minimizing time and distance in robotic path planning and sensor networking. Dr. Liu has also explored partition dimensions of series-parallel graphs and 2-metric resolvability in rotationally-symmetric graphs, extending the theory to more complex network structures. In 2024, he expanded into visual feature extraction and tracking for robotics, demonstrating the breadth of his applied interests. With over 100 citations across his most-cited works, Dr. Liu’s research bridges pure combinatorics and practical engineering, offering elegant solutions to real-world challenges in intelligent systems and combinatorial optimization.
Research Focus
Key Achievements
Top Papers
- 1Bounds on the Partition Dimension of Convex Polytopes35 citations · 2020
- 2Sharp Bounds of Local Fractional Metric Dimensions of Connected Networks34 citations · 2020
- 3Partition dimension of certain classes of series parallel graphs17 citations · 2019
- 4Visual Feature Extraction and Tracking Method Based on Corner Flow Detection14 citations · 2024
- 5On 2-metric resolvability in rotationally-symmetric graphs5 citations · 2021
- 6