EECS Seminar: Learning Higher-order Interactions with Graph Volterra Models
Faculty of Electrical Engineering, Mathematics and Computer Science
Delft University of Technology
Abstract: Complex network processes are known to be driven not only by pairwise interactions but also by the interactions of small groups of tightly connected nodes, sometimes called higher-order interactions. So, identifying these higher-order interactions becomes paramount to gain insight in the nature of such processes. While predicting pairwise nodal interactions (links) from network data is a well-studied problem, the identification of higher-order interactions (higher-order links) has not been fully understood. In this talk, we review several approaches that have been proposed for addressing this task and examine their respective limitations. Furthermore, cross-fertilizing ideas from Volterra series and linear structural equation models, we introduce a principled method that can capture higher-order interactions among nodes, the so-called graph Volterra model. The proposed approach can identify higher-order interactions among nodes by the respective graph Volterra kernels. To motivate the adoption of our new model, we demonstrate its performance on real data for enhancing topology identification in smart grids and higher-order link prediction in social networks.
Bio: Geert Leus received a master's degree and doctorate in electrical engineering from the KU Leuven, Belgium, in June 1996 and May 2000, respectively. Leus is now an Antoni van Leeuwenhoek Professor of electrical engineering, mathematics and computer science at the Delft University of Technology, Netherlands. His research interests are in the broad area of signal processing, with a specific focus on wireless communications, array processing, sensor networks and graph signal processing. Leus received a 2002 IEEE Signal Processing Society Young Author Best Paper Award and a 2005 IEEE Signal Processing Society Best Paper Award. He is a fellow of IEEE and EURASIP. Leus was a member-at-large of the Board of Governors of the IEEE Signal Processing Society, chair of the IEEE Signal Processing for Communications and Networking Technical Committee, member of the IEEE Sensor Array and Multichannel Technical Committee, member of the IEEE Big Data Special Interest Group and editor in chief of the EURASIP Journal on Advances in Signal Processing. He was also on the editorial boards of the IEEE Transactions on Signal Processing, IEEE Transactions on Wireless Communications, IEEE Signal Processing Letters and EURASIP Journal on Advances in Signal Processing. Currently, he is chair of the EURASIP Technical Area Committee on Signal Processing for Multisensor Systems, member of the IEEE Signal Processing Theory and Methods Technical Committee, associate editor of Foundations and Trends in Signal Processing, and editor in chief of EURASIP Signal Processing.
Host: Yanning Shen