Semi-Supervised Learning of Processes over Multi-Relational Graphs

Qin Lu, Vassilis N. Ioannidis, Georgios B. Giannakis

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

1 Scopus citations

Abstract

Semi-supervised learning (SSL) of dynamic processes over graphs is encountered in several applications of network science. Most of the existing approaches are unable to handle graphs with multiple relations, which arise in various real-world networks. This work deals with SSL of dynamic processes over multi-relational graphs (MRGs). Towards this end, a structured dynamical model is introduced to capture the spatio-temporal nature of dynamic graph processes, and incorporate contributions from multiple relations of the graph in a probabilistic fashion. Given nodal samples over a subset of nodes and the MRG, the expectation-maximization (EM) algorithm is adapted to extrapolate nodal features over unobserved nodes, and infer the contributions from the multiple relations in the MRG simultaneously. Experiments with real data showcase the merits of the proposed approach.

Original languageEnglish (US)
Title of host publication2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5560-5564
Number of pages5
ISBN (Electronic)9781509066315
DOIs
StatePublished - May 2020
Event2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Barcelona, Spain
Duration: May 4 2020May 8 2020

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2020-May
ISSN (Print)1520-6149

Conference

Conference2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020
Country/TerritorySpain
CityBarcelona
Period5/4/205/8/20

Bibliographical note

Funding Information:
This work was supported in part by NSF grants 1508993, 1711471, and 1901134.

Publisher Copyright:
© 2020 IEEE.

Keywords

  • Dynamic graph processes
  • EM
  • multi-relational graphs
  • semi-supervised learning

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