Linear structural vector autoregressive models (SVARMs) have well-documented merits for topology inference of directional graphs emerging in diverse applications, including gene-regulatory, brain, and social networks. Although simple and tractable, linear SVARMs cannot capture nonlinearities that are inherent to complex systems, such as the human brain, that can also vary over time. Given nodal measurements, these considerations motivate the dynamic nonlinear SVARM approach developed here to track the possibly directed and dynamic nonlinear interactions among network nodes. For slow-varying topologies, nonlinear model parameters are estimated via functional stochastic gradient descent. Numerical tests showcase the effectiveness of the novel algorithms in unveiling sparse dynamically-evolving topologies.
|Original language||English (US)|
|Title of host publication||2018 IEEE Data Science Workshop, DSW 2018 - Proceedings|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||5|
|State||Published - Aug 17 2018|
|Event||2018 IEEE Data Science Workshop, DSW 2018 - Lausanne, Switzerland|
Duration: Jun 4 2018 → Jun 6 2018
|Name||2018 IEEE Data Science Workshop, DSW 2018 - Proceedings|
|Other||2018 IEEE Data Science Workshop, DSW 2018|
|Period||6/4/18 → 6/6/18|
Bibliographical noteFunding Information:
† Work was supported by NSF grants 1500713, 1514056, and 1711471.
- Network topology inference
- structural vector autoregressive models