Learning causal information flow structures in multi-layer networks

Başak Guler; Aylin Yener; Ananthram Swami

doi:10.1109/GlobalSIP.2016.7906059

Learning causal information flow structures in multi-layer networks

Başak Guler
, Aylin Yener
, Ananthram Swami

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

2 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 language	English (US)
Title of host publication	2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1340-1344
Number of pages	5
ISBN (Electronic)	9781509045457
DOIs	https://doi.org/10.1109/GlobalSIP.2016.7906059
State	Published - Apr 19 2017
Event	2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Washington, United States Duration: Dec 7 2016 → Dec 9 2016

Publication series

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

Other

Other	2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016
Country/Territory	United States
City	Washington
Period	12/7/16 → 12/9/16

All Science Journal Classification (ASJC) codes

Signal Processing
Computer Networks and Communications

Access to Document

10.1109/GlobalSIP.2016.7906059

Cite this

Guler, B., Yener, A., & Swami, A. (2017). Learning causal information flow structures in multi-layer networks. In 2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings (pp. 1340-1344). Article 7906059 (2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/GlobalSIP.2016.7906059

Guler, Başak ; Yener, Aylin ; Swami, Ananthram. / Learning causal information flow structures in multi-layer networks. 2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 1340-1344 (2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings).

@inproceedings{7363fe23da5b44d284bc4669c7e2aa54,

title = "Learning causal information flow structures in multi-layer networks",

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.",

author = "Ba{\c s}ak Guler and Aylin Yener and Ananthram Swami",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 ; Conference date: 07-12-2016 Through 09-12-2016",

year = "2017",

month = apr,

day = "19",

doi = "10.1109/GlobalSIP.2016.7906059",

language = "English (US)",

series = "2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1340--1344",

booktitle = "2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings",

address = "United States",

}

Guler, B, Yener, A & Swami, A 2017, Learning causal information flow structures in multi-layer networks. in 2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings., 7906059, 2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 1340-1344, 2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016, Washington, United States, 12/7/16. https://doi.org/10.1109/GlobalSIP.2016.7906059

Learning causal information flow structures in multi-layer networks. / Guler, Başak; Yener, Aylin; Swami, Ananthram.
2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. p. 1340-1344 7906059 (2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings).

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

TY - GEN

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AU - Guler, Başak

AU - Yener, Aylin

AU - Swami, Ananthram

PY - 2017/4/19

Y1 - 2017/4/19

N2 - 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.

AB - 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.

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UR - https://www.scopus.com/pages/publications/85019270214#tab=citedBy

U2 - 10.1109/GlobalSIP.2016.7906059

DO - 10.1109/GlobalSIP.2016.7906059

M3 - Conference contribution

AN - SCOPUS:85019270214

T3 - 2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings

SP - 1340

EP - 1344

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

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016

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ER -

Guler B, Yener A, Swami A. Learning causal information flow structures in multi-layer networks. In 2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2017. p. 1340-1344. 7906059. (2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings). doi: 10.1109/GlobalSIP.2016.7906059

Learning causal information flow structures in multi-layer networks

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