iFiG: Individually Fair Multi-view Graph Clustering

Yian Wang; Jian Kang; Yinglong Xia; Jiebo Luo; Hanghang Tong

doi:10.1109/BigData55660.2022.10020548

iFiG: Individually Fair Multi-view Graph Clustering

Yian Wang, Jian Kang, Yinglong Xia, Jiebo Luo, Hanghang Tong

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

Abstract

A multi-view graph comprises multiple single-view graphs with the same set of nodes but different types of edges. In many real-world applications, graphs are often collected from multiple sources, forming multi-view graphs. For example, users could have accounts on numerous social platforms like Facebook and Twitter; the infrastructure network of cities exhibits different topologies considering different types of infrastructures (e.g., power grid, road network). Up to now, researchers have proposed a variety of multi-view graph mining models, including clustering [1] , embedding [2] , and graph neural networks [3].

Original language	English (US)
Title of host publication	Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022
Editors	Shusaku Tsumoto, Yukio Ohsawa, Lei Chen, Dirk Van den Poel, Xiaohua Hu, Yoichi Motomura, Takuya Takagi, Lingfei Wu, Ying Xie, Akihiro Abe, Vijay Raghavan
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	329-338
Number of pages	10
ISBN (Electronic)	9781665480451
DOIs	https://doi.org/10.1109/BigData55660.2022.10020548
State	Published - 2022
Event	2022 IEEE International Conference on Big Data, Big Data 2022 - Osaka, Japan Duration: Dec 17 2022 → Dec 20 2022

Publication series

Name	Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022

Conference

Conference	2022 IEEE International Conference on Big Data, Big Data 2022
Country/Territory	Japan
City	Osaka
Period	12/17/22 → 12/20/22

Keywords

Clustering
individual fairness
multi-objective optimization

ASJC Scopus subject areas

Modeling and Simulation
Computer Networks and Communications
Information Systems
Information Systems and Management
Safety, Risk, Reliability and Quality
Control and Optimization

Online availability

10.1109/BigData55660.2022.10020548

Library availability

Discover UIUC Full Text

Cite this

Wang, Y., Kang, J., Xia, Y., Luo, J., & Tong, H. (2022). iFiG: Individually Fair Multi-view Graph Clustering. In S. Tsumoto, Y. Ohsawa, L. Chen, D. Van den Poel, X. Hu, Y. Motomura, T. Takagi, L. Wu, Y. Xie, A. Abe, & V. Raghavan (Eds.), Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022 (pp. 329-338). (Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData55660.2022.10020548

iFiG: Individually Fair Multi-view Graph Clustering. / Wang, Yian; Kang, Jian; Xia, Yinglong et al.
Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022. ed. / Shusaku Tsumoto; Yukio Ohsawa; Lei Chen; Dirk Van den Poel; Xiaohua Hu; Yoichi Motomura; Takuya Takagi; Lingfei Wu; Ying Xie; Akihiro Abe; Vijay Raghavan. Institute of Electrical and Electronics Engineers Inc., 2022. p. 329-338 (Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022).

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

Wang, Y, Kang, J, Xia, Y, Luo, J & Tong, H 2022, iFiG: Individually Fair Multi-view Graph Clustering. in S Tsumoto, Y Ohsawa, L Chen, D Van den Poel, X Hu, Y Motomura, T Takagi, L Wu, Y Xie, A Abe & V Raghavan (eds), Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022. Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022, Institute of Electrical and Electronics Engineers Inc., pp. 329-338, 2022 IEEE International Conference on Big Data, Big Data 2022, Osaka, Japan, 12/17/22. https://doi.org/10.1109/BigData55660.2022.10020548

Wang Y, Kang J, Xia Y, Luo J, Tong H. iFiG: Individually Fair Multi-view Graph Clustering. In Tsumoto S, Ohsawa Y, Chen L, Van den Poel D, Hu X, Motomura Y, Takagi T, Wu L, Xie Y, Abe A, Raghavan V, editors, Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 329-338. (Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022). doi: 10.1109/BigData55660.2022.10020548

Wang, Yian ; Kang, Jian ; Xia, Yinglong et al. / iFiG : Individually Fair Multi-view Graph Clustering. Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022. editor / Shusaku Tsumoto ; Yukio Ohsawa ; Lei Chen ; Dirk Van den Poel ; Xiaohua Hu ; Yoichi Motomura ; Takuya Takagi ; Lingfei Wu ; Ying Xie ; Akihiro Abe ; Vijay Raghavan. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 329-338 (Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022).

@inproceedings{3404f982be6b41ceb77ef4496d6d3f2c,

title = "iFiG: Individually Fair Multi-view Graph Clustering",

abstract = "A multi-view graph comprises multiple single-view graphs with the same set of nodes but different types of edges. In many real-world applications, graphs are often collected from multiple sources, forming multi-view graphs. For example, users could have accounts on numerous social platforms like Facebook and Twitter; the infrastructure network of cities exhibits different topologies considering different types of infrastructures (e.g., power grid, road network). Up to now, researchers have proposed a variety of multi-view graph mining models, including clustering [1] , embedding [2] , and graph neural networks [3].",

keywords = "Clustering, individual fairness, multi-objective optimization",

author = "Yian Wang and Jian Kang and Yinglong Xia and Jiebo Luo and Hanghang Tong",

note = "ACKNOWLEDGMENT This work is supported by NSF (1947135, and 2134079), the NSF Program on Fairness in AI in collaboration with Amazon (1939725), DARPA (HR001121C0165), NIFA (2020-67021-32799), and ARO (W911NF2110088). The content of the information in this document does not necessarily reflect the position or the policy of the Government or Amazon, and no official endorsement should be inferred. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on.; 2022 IEEE International Conference on Big Data, Big Data 2022 ; Conference date: 17-12-2022 Through 20-12-2022",

year = "2022",

doi = "10.1109/BigData55660.2022.10020548",

language = "English (US)",

series = "Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022",

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

pages = "329--338",

editor = "Shusaku Tsumoto and Yukio Ohsawa and Lei Chen and {Van den Poel}, Dirk and Xiaohua Hu and Yoichi Motomura and Takuya Takagi and Lingfei Wu and Ying Xie and Akihiro Abe and Vijay Raghavan",

booktitle = "Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022",

address = "United States",

}

TY - GEN

T1 - iFiG

T2 - 2022 IEEE International Conference on Big Data, Big Data 2022

AU - Wang, Yian

AU - Kang, Jian

AU - Xia, Yinglong

AU - Luo, Jiebo

AU - Tong, Hanghang

N1 - ACKNOWLEDGMENT This work is supported by NSF (1947135, and 2134079), the NSF Program on Fairness in AI in collaboration with Amazon (1939725), DARPA (HR001121C0165), NIFA (2020-67021-32799), and ARO (W911NF2110088). The content of the information in this document does not necessarily reflect the position or the policy of the Government or Amazon, and no official endorsement should be inferred. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on.

PY - 2022

Y1 - 2022

N2 - A multi-view graph comprises multiple single-view graphs with the same set of nodes but different types of edges. In many real-world applications, graphs are often collected from multiple sources, forming multi-view graphs. For example, users could have accounts on numerous social platforms like Facebook and Twitter; the infrastructure network of cities exhibits different topologies considering different types of infrastructures (e.g., power grid, road network). Up to now, researchers have proposed a variety of multi-view graph mining models, including clustering [1] , embedding [2] , and graph neural networks [3].

AB - A multi-view graph comprises multiple single-view graphs with the same set of nodes but different types of edges. In many real-world applications, graphs are often collected from multiple sources, forming multi-view graphs. For example, users could have accounts on numerous social platforms like Facebook and Twitter; the infrastructure network of cities exhibits different topologies considering different types of infrastructures (e.g., power grid, road network). Up to now, researchers have proposed a variety of multi-view graph mining models, including clustering [1] , embedding [2] , and graph neural networks [3].

KW - Clustering

KW - individual fairness

KW - multi-objective optimization

UR - http://www.scopus.com/inward/record.url?scp=85147927121&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85147927121&partnerID=8YFLogxK

U2 - 10.1109/BigData55660.2022.10020548

DO - 10.1109/BigData55660.2022.10020548

M3 - Conference contribution

AN - SCOPUS:85147927121

T3 - Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022

SP - 329

EP - 338

BT - Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022

A2 - Tsumoto, Shusaku

A2 - Ohsawa, Yukio

A2 - Chen, Lei

A2 - Van den Poel, Dirk

A2 - Hu, Xiaohua

A2 - Motomura, Yoichi

A2 - Takagi, Takuya

A2 - Wu, Lingfei

A2 - Xie, Ying

A2 - Abe, Akihiro

A2 - Raghavan, Vijay

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 17 December 2022 through 20 December 2022

ER -

iFiG: Individually Fair Multi-view Graph Clustering

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Online availability

Library availability

Related links

Fingerprint

Cite this