Report Title: Intelligent Bibliometric Networks and Applications in Science, Technology, and Innovation

Time: May 23rd (Tuesday) 14:00-16:00 PM (GMT+08:00)

Location: Main Building 317

Reported by: Dr Yi Zhang, University of Technology Sydney

Introduction to the speaker:

Dr. Zhang Yi is now a senior lecturer (tenure) of the Australian Institute of Artificial Intelligence at the University of Technology Sydney, a winner of the DECRA (Discovery Early Career Research Award) Fund of the Australian Research Council in 2019, and a leader in the discipline of library and information science in Australia at the Australian's Research Award 2023. He holds double doctorates in Management science and engineering (Beijing Institute of Technology) and software engineering (University of Technology Sydney). He is a Visiting scholar (2011-2012) from the School of Public Policy, Georgia Tech University.

Dr. Zhang Yi focuses on the research in the field of Bibliometrics and technology Innovation management, emphasizing the theoretical framework and method innovation of intelligent literature intelligence oriented to technology Innovation management. Published over 100 academic papers (including 4 highly cited papers from 2017 to 2022). His Google Scholar paper has been cited more than 2400 times, with an H Index of 24.

Dr. Zhang Yi currently serves as the deputy editor in chief of the Journal of Technical Forecasting and Social Change and Scientology, an editorial board member of the IEEE Transactions on Engineering Management Journal, and an advisory member of the Elsevier International Center for Scientific Evaluation Global Committee.

Introduction to report content:This talk is to introduce the development and applications of intelligent bibliometric networks for supporting broad science, technology, and innovation studies. Within a dynamic and heterogeneous bibliometric network, this talk is to introduce some efforts on capturing network functionalities, measuring functional interactions, and monitoring system dynamics. Specifically, focusing on the interest of knowledge trajectory recommendation, this talk is to introduce a current development of diffusion-based bi-layer network analytics, in which a novel link prediction approach with a diffusion strategy to reflect real-world academic activities in knowledge sharing between co-authors and knowledge diffusing between similar research topics. This talk will also conclude current research challenges and future directions in line with intelligent bibliometric network analytics.

(Undertaken by: Knowledge Management and Data Analysis Laboratory, Research and Academic Center)