Semi-Supervised Tracking of Dynamic Processes over Switching Graphs

Qin Lu, Vassilis N. Ioannidis, Georgios B Giannakis

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

Abstract

Several network science applications involve nodal processes with dynamics dependent on the underlying graph topology that can possibly jump over discrete states. The connectivity in dynamic brain networks for instance, switches among candidate topologies, each corresponding to a different emotional state. In this context, the present work relies on limited nodal observations to perform semi-supervised tracking of dynamic processes over switching graphs. To this end, leveraging what is termed interacting multi-graph model (IMGM), a scalable online Bayesian approach is developed to track the active graph topology and dynamic nodal process. Numerical tests with synthetic and real datasets demonstrate the merits of the novel approach.

Original languageEnglish (US)
Title of host publication2019 IEEE Data Science Workshop, DSW 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages64-68
Number of pages5
ISBN (Electronic)9781728107080
DOIs
StatePublished - Jun 2019
Event2019 IEEE Data Science Workshop, DSW 2019 - Minneapolis, United States
Duration: Jun 2 2019Jun 5 2019

Publication series

Name2019 IEEE Data Science Workshop, DSW 2019 - Proceedings

Conference

Conference2019 IEEE Data Science Workshop, DSW 2019
CountryUnited States
CityMinneapolis
Period6/2/196/5/19

Fingerprint

Topology
Brain
Switches

Keywords

  • Bayesian tracking
  • Dynamic graphs

Cite this

Lu, Q., Ioannidis, V. N., & Giannakis, G. B. (2019). Semi-Supervised Tracking of Dynamic Processes over Switching Graphs. In 2019 IEEE Data Science Workshop, DSW 2019 - Proceedings (pp. 64-68). [8755553] (2019 IEEE Data Science Workshop, DSW 2019 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DSW.2019.8755553

Semi-Supervised Tracking of Dynamic Processes over Switching Graphs. / Lu, Qin; Ioannidis, Vassilis N.; Giannakis, Georgios B.

2019 IEEE Data Science Workshop, DSW 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. p. 64-68 8755553 (2019 IEEE Data Science Workshop, DSW 2019 - Proceedings).

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

Lu, Q, Ioannidis, VN & Giannakis, GB 2019, Semi-Supervised Tracking of Dynamic Processes over Switching Graphs. in 2019 IEEE Data Science Workshop, DSW 2019 - Proceedings., 8755553, 2019 IEEE Data Science Workshop, DSW 2019 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 64-68, 2019 IEEE Data Science Workshop, DSW 2019, Minneapolis, United States, 6/2/19. https://doi.org/10.1109/DSW.2019.8755553
Lu Q, Ioannidis VN, Giannakis GB. Semi-Supervised Tracking of Dynamic Processes over Switching Graphs. In 2019 IEEE Data Science Workshop, DSW 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2019. p. 64-68. 8755553. (2019 IEEE Data Science Workshop, DSW 2019 - Proceedings). https://doi.org/10.1109/DSW.2019.8755553
Lu, Qin ; Ioannidis, Vassilis N. ; Giannakis, Georgios B. / Semi-Supervised Tracking of Dynamic Processes over Switching Graphs. 2019 IEEE Data Science Workshop, DSW 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 64-68 (2019 IEEE Data Science Workshop, DSW 2019 - Proceedings).
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