Semi-Supervised Tracking of Dynamic Processes over Switching Graphs

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

doi:10.1109/DSW.2019.8755553

Semi-Supervised Tracking of Dynamic Processes over Switching Graphs

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

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

6 Scopus citations

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 language	English (US)
Title of host publication	2019 IEEE Data Science Workshop, DSW 2019 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	64-68
Number of pages	5
ISBN (Electronic)	9781728107080
DOIs	https://doi.org/10.1109/DSW.2019.8755553
State	Published - Jun 2019
Event	2019 IEEE Data Science Workshop, DSW 2019 - Minneapolis, United States Duration: Jun 2 2019 → Jun 5 2019

Publication series

Name	2019 IEEE Data Science Workshop, DSW 2019 - Proceedings

Conference

Conference	2019 IEEE Data Science Workshop, DSW 2019
Country/Territory	United States
City	Minneapolis
Period	6/2/19 → 6/5/19

Bibliographical note

Funding Information:
This paper dealt with tracking dynamic graph processes that evolve over switching graph topologies. Given observations at a subset of nodes and candidate mode-conditioned topologies, a scalable Bayesian algorithm, termed IMGM, was introduced to learn the dynamic graph processes and discrete network modes online. Numerical tests on synthetic and real data corroborated the performance of the IMGM algorithm. Acknowledgement. This work was supported by NSF grants 1442686, 1508993, and 1711471.

Publisher Copyright:
© 2019 IEEE.

Keywords

Bayesian tracking
Dynamic graphs

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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 proceeding › Conference 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

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Semi-Supervised Tracking of Dynamic Processes over Switching Graphs

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