Data Science & AI Lecture Series
Powers of magnetic graph matrix: Fourier spectrum, walk compression, and applications
David Gleich
Upcoming
Powers of magnetic graph matrix: Fourier spectrum, walk compression, and applications
| When | Friday, October 9, 2026, 12:00 PM – 1:00 PM (MT) |
|---|---|
| Where | WEB 2250 |
| Zoom | Join online |
Abstract
Magnetic graph matrices are powerful tools for modeling quantum systems and directed networks, but their application in network analysis has been limited by a lack of combinatorial understanding. We present a combinatorial interpretation that fundamentally reveals how these matrices encode local network structure. We further show that this structure information is highly compressible in real-world networks, enabling accurate approximations from a small number of magnetic potentials. This fresh foundation also unlocks further applications, like identifying crucial network motifs (e.g., frustrated directed cycles such as feed-forward loops) and enhancing directed link prediction.
Link to paper: https://www.pnas.org/doi/10.1073/pnas.2516664123
Speaker
David Gleich
Purdue University
David Gleich is a Professor and University Faculty Scholar in the Computer Science Department at Purdue University whose research is on novel models and fast large-scale algorithms for data-driven scientific computing including scientific data analysis, bioinformatics, and network analysis. He is committed to making software available based on this research. Gleich has received a number of awards for his research including a SIAM Outstanding Publication prize (2018), a Sloan Research Fellowship (2016), an NSF CAREER Award (2011), the John von Neumann post-doctoral fellowship at Sandia National Laboratories in Livermore CA (2009). His research has been funded by the NSF, DOE, DARPA, IARPA, and NASA. He was selected as a Fellow of SIAM in 2025.
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Tags: algorithms & theory networks & graphs
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