Abstract
Linear structural equation models (SEMs) have been very successful in identifying the topology of complex graphs, such as those representing tactical, social and brain networks. The rising popularity of multilayer networks, presents the need for tools that are tailored to leverage the layered structure of the underlying network. To this end, a multilayer SEM is put forth, to infer causal relations between nodes belonging to multilayer networks. An efficient algorithm based on the alternating direction method of multipliers (ADMM) is developed, and preliminary tests on synthetic as well as real data demonstrate the effectiveness of the proposed approach.
Original language | English (US) |
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Title of host publication | 2017 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2017 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 898-903 |
Number of pages | 6 |
ISBN (Electronic) | 9781538627846 |
DOIs | |
State | Published - Nov 20 2017 |
Event | 2017 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2017 - Atlanta, United States Duration: May 1 2017 → May 4 2017 |
Publication series
Name | 2017 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2017 |
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Other
Other | 2017 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2017 |
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Country/Territory | United States |
City | Atlanta |
Period | 5/1/17 → 5/4/17 |
Bibliographical note
Publisher Copyright:© 2017 IEEE.
Keywords
- Multilayer networks
- Structural Equation Models
- Topology inference