Attend LIONS seminar with speaker Yixuan He, Oct. 2

Join the Learning, Information, Optimization, Networks and Statistics, or LIONS, group for a seminar with Yixuan He, assistant professor in the School of Mathematical and Natural Sciences with a joint appointment in the School of Computing and Augmented Intelligence, part of the Ira A. Fulton Schools of Engineering at ASU. He completed her doctoral degree at Department of Statistics, University of Oxford, in 2024. During her doctoral studies, she was honored to be selected as a Clarendon Scholar fully funded by University of Oxford. He is one of the program chairs for the 5th Learning on Graphs, or LoG, conference in 2026, an international conference on learning on graphs and geometry, after being the local host for the 4th LoG conference held at ASU in 2025. She is the lead organizer of the ASU AI seminar and one of the core organizers for the annual ASU Machine Learning Day.

Graph neural networks learn from relations, but many standard models treat every edge as an unsigned, undirected connection. In social, financial and biological networks, edge sign and direction can carry the information needed for clustering or prediction. This seminar will begin with a short introduction to graph neural networks, then describe how signed and directed methods preserve this structure. This sets up a broader question: when can a graph representation learned before the final task be reused on another graph or with few labels? He will discuss two recent theoretical perspectives, one on transfer across graph domains and one on how link prediction can support linear community detection. She will then present TopoDIG and TopoSIGN, which combine edge-aware graph encoders with persistent topological features for pre-training and prompt-based adaptation on directed and signed networks.

Visit the LIONS Seminar website for more information.

LIONS seminar: Graph Neural Networks and Foundation Models for Complex Networks
Friday, Oct. 2, 2026
1:30–2:30 p.m.
Goldwater Center (GWC) 487, Tempe campus [map]
Attend online via Zoom