About Our SIG
SIG-MET is the Association for Information Science & Technology (ASIS&T) Special Interest Group for the measurement of information production and use. It encourages the development and networking of all those interested in the measurement of information. It encompasses not only bibliometrics, scientometrics and informetrics, but also measurement of the Web and the Internet, applications running on these platforms, and metrics related to network analysis, visualization, scholarly communication and the design and operation of Information Retrieval Systems. SIG/MET will facilitate activities to encourage the promotion, research and application of metrics topics. Academics, practitioners, commercial providers, government representatives, and any other interested persons are welcome.
Forthcoming Event
SIG-MET Distinguished Speaker Series: Lecture by Dr. Yong-Yeol (YY) Ahn
Speaker: Dr. Yong-Yeol (YY) Ahn
Dr. Yong-Yeol (YY) Ahn is a network and data scientist whose work combines network science, machine learning, and the study of complex social, biological, and information systems. He is a Quantitative Foundation Distinguished Professor at the University of Virginia’s School of Data Science. Before joining UVA, he was a Professor at Indiana University’s CNetS, Luddy School of Informatics, Computing, and Engineering and a Visiting Professor at MIT. Earlier, he worked as a postdoctoral research associate at the Center for Complex Network Research at Northeastern University and as a visiting researcher at the Center for Cancer Systems Biology at Dana-Farber Cancer Institute after completing his PhD in Statistical Physics from KAIST. His research focuses on the architectures of complex systems—how networks shape behavior, cognition, and scientific progress—and on developing methods in network analysis, machine learning, and natural language processing to investigate these mechanisms at scale. He is the co-author of Working with Network Data. His work has been recognized with several honors, including the Microsoft Research Faculty Fellowship.
Title: The Geometry of Science
What would scientometrics look like if scientific ideas lived in a concrete physical space? Deep representation learning now allows us to imagine such a shared knowledge embedding space where scholarly works, ideas, and other entities can coexist. Yet these powerful models are often black boxes, hard to translate into interpretable metrics. In this talk, I show that a fundamental understanding of embedding methods can make this space interpretable, allowing its geometry to be measured directly. The flow of scientists between institutions follows a gravity law governed by distance in the space, and scientific disruption can be captured through the geometric displacement of a field's trajectory — without relying on sparse local citation links. This embedding-based framework may offer a unified language for describing how science attracts, disrupts, and moves, complementing traditional citation-based indicators.
Lecture Guide:
Registration - https://www2.asist.org/ap/Events/Register/xRFXn32sMCYC7
Location - Online event, the lecture link will be sent to registered attendees via email.
Time - 9:00-10:00 AM (EDT) USA time on Tuesday, March 17th, 2026.
Organizers:
ASIS&T SIG-MET
Faculty of Education, The University of Hong Kong
School of Information Management, Sun Yat-sun University
Learn More
Publications & Resources
Visit the SIG-MET member-only resource archive.
How to Join
To be a member, one first needs to be a member of the Association for Information Science & Technology (ASIS&T). When completing the Membership Application Form, select the special interest group (SIG) of your choice.