Info-Clustering: An Efficient Algorithm by Network Information Flow

Chung Chan; Ali Al-Bashabsheh; Qiaoqiao Zhou

doi:10.1109/ITA.2017.8023445

Info-Clustering: An Efficient Algorithm by Network Information Flow

Chung Chan^*, Ali Al-Bashabsheh, Qiaoqiao Zhou

^*Corresponding author for this work

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

2 Citations (Scopus)

Abstract

Motivated by the fact that entities in a social network or biological system often interact by exchanging information, we propose an efficient info-clustering algorithm that can group entities into communities using a parametric max-flow algorithm. This is a meaningful special case of the info-clustering paradigm where the dependency structure is graphical and can be learned readily from data.

Original language	English
Title of host publication	2017 Information Theory and Applications Workshop (ITA)
Publisher	IEEE
ISBN (Electronic)	978-1-5090-5293-6
ISBN (Print)	978-1-5090-5294-3
DOIs	https://doi.org/10.1109/ITA.2017.8023445
Publication status	Published - 2017
Externally published	Yes
Event	2017 Information Theory and Applications Workshop, ITA - San Diego, United States Duration: 12 Feb 2017 → 17 Feb 2017 http://ita.ucsd.edu/workshop/17/

Publication series

Name	Information Theory and Applications Workshop, ITA
Publisher	IEEE

Workshop

Workshop	2017 Information Theory and Applications Workshop, ITA
Place	United States
City	San Diego
Period	12/02/17 → 17/02/17
Internet address	http://ita.ucsd.edu/workshop/17/

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10.1109/ITA.2017.8023445

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@inproceedings{9b9bb0dc6b1a4944854d9bac26c68691,

title = "Info-Clustering: An Efficient Algorithm by Network Information Flow",

abstract = "Motivated by the fact that entities in a social network or biological system often interact by exchanging information, we propose an efficient info-clustering algorithm that can group entities into communities using a parametric max-flow algorithm. This is a meaningful special case of the info-clustering paradigm where the dependency structure is graphical and can be learned readily from data.",

author = "Chung Chan and Ali Al-Bashabsheh and Qiaoqiao Zhou",

year = "2017",

doi = "10.1109/ITA.2017.8023445",

language = "English",

isbn = "978-1-5090-5294-3",

series = "Information Theory and Applications Workshop, ITA",

publisher = "IEEE",

booktitle = "2017 Information Theory and Applications Workshop (ITA)",

address = "United States",

note = "2017 Information Theory and Applications Workshop, ITA ; Conference date: 12-02-2017 Through 17-02-2017",

url = "http://ita.ucsd.edu/workshop/17/",

}

Chan, C, Al-Bashabsheh, A & Zhou, Q 2017, Info-Clustering: An Efficient Algorithm by Network Information Flow. in 2017 Information Theory and Applications Workshop (ITA)., 8023445, Information Theory and Applications Workshop, ITA, IEEE, 2017 Information Theory and Applications Workshop, ITA, San Diego, California, United States, 12/02/17. https://doi.org/10.1109/ITA.2017.8023445

Info-Clustering: An Efficient Algorithm by Network Information Flow. / Chan, Chung; Al-Bashabsheh, Ali; Zhou, Qiaoqiao.
2017 Information Theory and Applications Workshop (ITA). IEEE, 2017. 8023445 (Information Theory and Applications Workshop, ITA).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Info-Clustering

T2 - 2017 Information Theory and Applications Workshop, ITA

AU - Chan, Chung

AU - Al-Bashabsheh, Ali

AU - Zhou, Qiaoqiao

PY - 2017

Y1 - 2017

N2 - Motivated by the fact that entities in a social network or biological system often interact by exchanging information, we propose an efficient info-clustering algorithm that can group entities into communities using a parametric max-flow algorithm. This is a meaningful special case of the info-clustering paradigm where the dependency structure is graphical and can be learned readily from data.

AB - Motivated by the fact that entities in a social network or biological system often interact by exchanging information, we propose an efficient info-clustering algorithm that can group entities into communities using a parametric max-flow algorithm. This is a meaningful special case of the info-clustering paradigm where the dependency structure is graphical and can be learned readily from data.

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UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85030999738&origin=recordpage

U2 - 10.1109/ITA.2017.8023445

DO - 10.1109/ITA.2017.8023445

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 978-1-5090-5294-3

T3 - Information Theory and Applications Workshop, ITA

BT - 2017 Information Theory and Applications Workshop (ITA)

PB - IEEE

Y2 - 12 February 2017 through 17 February 2017

ER -

Info-Clustering: An Efficient Algorithm by Network Information Flow

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