Kernel-Based Semi-Supervised Learning over Multilayer Graphs

Vassilis N. Ioannidis, Panagiotis A. Traganitis, Yanning Shen, Georgios B. Giannakis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations

Abstract

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 languageEnglish (US)
Title of host publication2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Print)9781538635124
DOIs
StatePublished - Aug 24 2018
Event19th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2018 - Kalamata, Greece
Duration: Jun 25 2018Jun 28 2018

Publication series

NameIEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC
Volume2018-June

Other

Other19th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2018
CountryGreece
CityKalamata
Period6/25/186/28/18

Keywords

  • Laplacian kernels
  • Semi-supervised learning
  • graph signal reconstruction
  • multilayer networks

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