Learning causal information flow structures in multi-layer networks

Başak Guler, Aylin Yener, Ananthram Swami

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

    1 Scopus citations

    Abstract

    We study causal influence structures between the patterns of a multilayer network. Multi-layer networks are networks in which different types of activities between users represent different types of edges, i.e., layers. We measure the causal influence between network patterns via directed information, and investigate how to learn the influence patterns when users can engage in interactions in multiple contexts. We evaluate the proposed methods using both synthetic and real-world datasets, and demonstrate that directed information measures can be utilized to identify the causal relations between network structures.

    Original languageEnglish (US)
    Title of host publication2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages1340-1344
    Number of pages5
    ISBN (Electronic)9781509045457
    DOIs
    StatePublished - Apr 19 2017
    Event2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Washington, United States
    Duration: Dec 7 2016Dec 9 2016

    Publication series

    Name2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings

    Other

    Other2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016
    Country/TerritoryUnited States
    CityWashington
    Period12/7/1612/9/16

    All Science Journal Classification (ASJC) codes

    • Signal Processing
    • Computer Networks and Communications

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