Title
Autoregressive graph Volterra models and applications
Author
Yang, Q.
Coutino, M.
Leus, G.
Giannakis, G.B.
Publication year
2023
Abstract
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. (C) 2023, The Author(s).
Subject
Graph inference
Higher-order interactions
Link prediction
Volterra series
Electric power distribution
Graph theory
Regression analysis
Auto-regressive
Graph inference
Graph-based learning
High-order
High-order interaction
Higher-order
Link prediction
Power networks
Volterra model
Volterra Series
Graphic methods
To reference this document use:
http://resolver.tudelft.nl/uuid:da218e43-9ecb-4e28-9466-d097b89745aa
TNO identifier
981998
Publisher
Springer Science and Business Media Deutschland GmbH
ISSN
1687-6172
Source
Eurasip Journal on Advances in Signal Processing, 2023 (2023)
Document type
article