Fellows Webinar: Understanding Citation Impact of Scientific Publications Through Ego-Centered Citation Networks

Our fellowship team from Peking University and Nanjing University will present the research they developed using CADRE in this webinar.

Watch the recording:
Event details:

When: June 17, 10 a.m.
Where: Online
Livestream link: https://iu.zoom.us/j/98314166436

About the research: The research team seeks to find the “deeper” and “broader” impact of network-based citation measurements in the scientific community. This project will determine the citation impact of scientific publications using an ego-centered citation network. Ego-centered citation networks contain the citing relationships between a publication and its citing publications, as well as the relationships within its citing publications. Researchers will use the entirety of the WoS and MAG data to establish empirical evidence in this project.

Project abstract: Breakthrough research plays an essential role in the advancement of science system. The identification and recognition of scientific breakthroughs is thus of extreme importance. We propose a citing-structure perspective for observing the unfolding of breakthrough research in the variations of knowledge structure. A series of network topology indictors are used to differentiate the citing networks of over 100 pairs of breakthrough papers and their control papers. 330 pairs of citing networks are subject to statistical tests for those indicators. The results show that compared with less ground-breaking papers, breakthrough papers show salient performance in terms of number of nodes, number of edges, average clustering coefficient, number of components, and average degree. Indicators such as network density, maximum betweenness centrality, and maximum closeness centrality, however, do not have a significantly discriminative power. It reveals that breakthrough papers have more connected citing networks than papers with less ground-breaking ideas. The characteristics have further been utilized for predicting scientific breakthroughs in the early stage.

About the researchers:

Before joining Peking University, he was a research fellow at the Center for Science of Science and Innovation (CSSI), Northwestern Institute on Complex Systems (NICO), and the Kellogg School of Management, Northwestern University working with Dr. Dashun Wang. He is doing research in the application aspect of big data analytics, with a particular focus on scholarly data mining. He has an undergraduate degree in information management and system from Peking University, an M.S. in data science, and a Ph.D. in informatics from Indiana University. At Indiana University, he was supervised by Professor Ying Ding.

His research interests mainly include Science of Science, Network Science, and Computational Social Science. He holds a doctor's degree in informatics and a bachelor's degree in management science, both from Nanjing University. He is a member of the International Society for Scientometrics and Informetrics (ISSI) and a member of the Association for Information Science and Technology (ASIS&T).

Read more about the fellowship team here: https://cadre.iu.edu/fellows/understanding-citation-impact-of-scientific-publications-through-ego-centered-citation-networks