Autoregressive graph Volterra models and applications

Autoregressive graph Volterra models and applications

Yang, Qiuling (University of Alberta)
Coutino, Mario (TNO)
Leus, G.J.T. (TU Delft Signal Processing Systems)
Giannakis, Georgios B. (University of Minnesota)

2023

Graph-based learning and estimation are fundamental problems in various applications involving power, social, and brain networks, to name a few. While learning pair-wise interactions in network data is a well-studied problem, discovering higher-order interactions among subsets of nodes is still not yet fully explored. To this end, encompassing and leveraging (non)linear structural equation models as well as vector autoregressions, this paper proposes autoregressive graph Volterra models (AGVMs) that can capture not only the connectivity between nodes but also higher-order interactions presented in the networked data. The proposed overarching model inherits the identifiability and expressibility of the Volterra series. Furthermore, two tailored algorithms based on the proposed AGVM are put forth for topology identification and link prediction in distribution grids and social networks, respectively. Real-data experiments on different real-world collaboration networks highlight the impact of higher-order interactions in our approach, yielding discernible differences relative to existing methods.

Higher-order interactions
Volterra series
Graph inference
Link prediction
OA-Fund TU Delft

http://resolver.tudelft.nl/uuid:22662eeb-bb16-4a82-b5a3-040b11aad7ec

https://doi.org/10.1186/s13634-022-00960-6

1687-6172

Eurasip Journal on Advances in Signal Processing, 2023 (1)

Institutional Repository

journal article

© 2023 Qiuling Yang, Mario Coutino, G.J.T. Leus, Georgios B. Giannakis