Networks arise in fields such as sociology, biology, and machine learning among others, to describe complex and often interdependent systems. These increasingly complex systems call for flexible network models that allow for multiple types of interactions among the agents (nodes) known as multilayer networks. A frequently encountered task entails inference of nodal processes across the network given values on a subset of nodes. The present contribution relies on graph kernels, to put forth a novel inference approach that accounts for linear and nonlinear dependencies among nodes and leverages the layered network structure. Numerical tests with synthetic as well as real data corroborate the effectiveness of the proposed kernel-based multilayer learning scheme.
|Original language||English (US)|
|Title of host publication||2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2018|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|State||Published - Aug 24 2018|
|Event||19th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2018 - Kalamata, Greece|
Duration: Jun 25 2018 → Jun 28 2018
|Name||IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC|
|Other||19th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2018|
|Period||6/25/18 → 6/28/18|
Bibliographical noteFunding Information:
The work in this paper was supported by NSF grants 1500713,1508993, 171141, and NIH 1R01GM104975-01.
© 2018 IEEE.
- Laplacian kernels
- Semi-supervised learning
- graph signal reconstruction
- multilayer networks