Mr. Huquan Kang | Knowledge Quantification | Best Researcher Award
Shanghai Jiao Tong Univeristy | China
Huquan Kang is a fourth-year Ph.D. candidate at Shanghai Jiao Tong University, specializing in knowledge measurement and the science of science. His research focuses on quantifying scientific knowledge, understanding its structure and dynamics, and exploring how knowledge flows between disciplines. Kang completed a one-year visiting scholar program at the Carlson School of Management, University of Minnesota, where he collaborated on interdisciplinary projects integrating scientometrics and computational modeling. With seven SCI/Scopus-indexed journal publications and a growing citation record, his work has been recognized across computer science and scientometric research communities. He also serves as a reviewer for top journals, including TNSE, Sigmetrics, TNET, and Mobihoc. Kang’s contributions aim to refine metrics for innovation evaluation, offering deeper insights into scientific progress and the evolution of research fields.
Professional Profile
Google Scholar
Scopus
Orcid
Education
Huquan Kang is pursuing his Ph.D. in Computer Science at Shanghai Jiao Tong University, where his dissertation research centers on computational models for measuring and analyzing scientific knowledge. He is in his fourth year of doctoral studies, having built a strong academic foundation in network science, scientometrics, and data-driven knowledge analysis. As part of his academic development, Kang spent one year as a visiting scholar at the Carlson School of Management, University of Minnesota, gaining exposure to interdisciplinary methodologies and international collaboration. His education emphasizes both theoretical understanding and applied research in areas such as citation networks, knowledge structuring, and innovation metrics. This blend of local and global academic experiences has equipped him with advanced skills in quantitative research and scholarly communication.
Professional Experience
Huquan Kang’s professional research journey began during his Ph.D. program at Shanghai Jiao Tong University, where he contributed to five significant research projects—three completed and two ongoing—focused on knowledge quantification and network-based science studies. His international exposure includes a visiting scholar tenure at the University of Minnesota’s Carlson School of Management, working on interdisciplinary research that bridged computer science, scientometrics, and management science. He has authored and co-authored seven SCI/Scopus-indexed publications in respected journals and serves as a reviewer for prominent academic outlets such as TNSE, Sigmetrics, TNET, and Mobihoc. In collaboration with leading scientists, Kang has advanced methods for analyzing knowledge structures and measuring scientific growth, contributing both theoretical models and applied insights to the global research community.
Awards
While Huquan Kang’s research career is still emerging, his contributions have already garnered recognition in academic circles. His publications in reputed SCI/Scopus-indexed journals have been cited 38 times, reflecting the growing influence of his work in scientometrics and computational knowledge analysis. His role as a peer reviewer for leading journals—including IEEE Transactions on Network Science and Engineering, ACM Sigmetrics, IEEE Transactions on Network and Service Management, and Mobihoc—demonstrates the trust placed in his expertise by the scholarly community. His selection as a visiting scholar at the University of Minnesota’s Carlson School of Management stands as a notable academic achievement, providing him with an opportunity to collaborate with globally recognized researchers. Although formal awards are yet to be recorded, Kang’s steady academic trajectory, impactful publications, and expanding research network position him for future honors and distinctions in the fields of knowledge measurement and science of science.
Research Interests
Huquan Kang’s research interests lie at the intersection of scientometrics, network science, and computational modeling. He focuses on developing quantitative frameworks to measure the structure, dynamics, and evolution of scientific knowledge. His work often explores how research fields grow, how innovations spread, and how knowledge moves between disciplines. Using citation network analysis, structural modeling, and interdisciplinary data integration, Kang aims to refine metrics for evaluating scholarly impact and innovation. He is particularly interested in understanding the balance between scientific productivity and the actual expansion of knowledge, as well as challenging the notion of exponential knowledge growth. By combining theoretical insights with empirical data, his research contributes to improving science policy, research evaluation systems, and our understanding of how knowledge ecosystems function globally.
Publication Top Notes
1.Quantifying knowledge from the perspective of information structurization
Year: 2023
Citations: 11
2. Efficient block propagation in wireless blockchain networks and its application in Bitcoin
Year: 2021
Citations: 9
3. On social network de-anonymization with communities: A maximum a posteriori perspective
Year: 2021
Citations: 7
4. Acemap: Knowledge discovery through academic graph
Year: 2024
Citations: 5
5. Measuring social network de-anonymizability by means of morphism property
Year: 2022
Citations: 3
6. A century of knowledge growth in sedimentology
Year: 2025
Citations: 1
7. Revisiting Network Value: Sublinear Knowledge Law
Year: 2023
Citations: 1
8. Exploring the Disproportion Between Scientific Productivity and Knowledge Amount
Year: 2021
Citations: 1
Conclusion
Huquan Kang represents a new generation of researchers who combine computational precision with a deep interest in the sociology and evolution of science. His doctoral work at Shanghai Jiao Tong University, complemented by international collaboration at the University of Minnesota, has positioned him at the forefront of scientometric research. With a growing portfolio of high-quality publications, an expanding citation record, and active involvement in peer review for prestigious journals, Kang continues to make strides in advancing methods for measuring and understanding knowledge systems. His interdisciplinary perspective allows him to bridge the gap between computer science and the science of science, offering innovative approaches to evaluating scholarly impact. As his research matures, Kang is poised to make significant contributions to global discussions on innovation and scientific growth.