Details
| Original language | English |
|---|---|
| Title of host publication | Advances in Knowledge Discovery and Data Mining |
| Subtitle of host publication | 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2024 |
| Editors | De-Nian Yang, Xing Xie, Vincent S. Tseng, Jian Pei, Jen-Wei Huang, Jerry Chun-Wei Lin |
| Publisher | Springer Science and Business Media Deutschland GmbH |
| Pages | 284-296 |
| Number of pages | 13 |
| ISBN (electronic) | 978-981-97-2242-6 |
| ISBN (print) | 9789819722419 |
| Publication status | Published - 25 Apr 2024 |
| Event | 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2024 - Taipei, Taiwan Duration: 7 May 2024 → 10 May 2024 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 14645 LNAI |
| ISSN (Print) | 0302-9743 |
| ISSN (electronic) | 1611-3349 |
| Name | Lecture Notes in Artificial Intelligence (LNAI) |
|---|---|
| Publisher | Springer Verlag |
| ISSN (Print) | 2945-9133 |
| ISSN (electronic) | 2945-9141 |
Abstract
Conventional fair graph clustering methods face two primary challenges: i) They prioritize balanced clusters at the expense of cluster cohesion by imposing rigid constraints, ii) Existing methods of both individual and group-level fairness in graph partitioning mostly rely on eigen decompositions and thus, generally lack interpretability. To address these issues, we propose iFairNMTF, an individual Fairness Nonnegative Matrix Tri-Factorization model with contrastive fairness regularization that achieves balanced and cohesive clusters. By introducing fairness regularization, our model allows for customizable accuracy-fairness trade-offs, thereby enhancing user autonomy without compromising the interpretability provided by nonnegative matrix tri-factorization. Experimental evaluations on real and synthetic datasets demonstrate the superior flexibility of iFairNMTF in achieving fairness and clustering performance.
Keywords
- Fair Graph Clustering, Fair Unsupervised Learning, Fair-Nonnegative Matrix Factorization, Individual Fairness
ASJC Scopus subject areas
- Mathematics(all)
- Theoretical Computer Science
- Computer Science(all)
- General Computer Science
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Advances in Knowledge Discovery and Data Mining: 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2024. ed. / De-Nian Yang; Xing Xie; Vincent S. Tseng; Jian Pei; Jen-Wei Huang; Jerry Chun-Wei Lin. Springer Science and Business Media Deutschland GmbH, 2024. p. 284-296 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14645 LNAI), (Lecture Notes in Artificial Intelligence (LNAI)).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Towards Cohesion-Fairness Harmony
T2 - 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2024
AU - Ghodsi, Siamak
AU - Seyedi, Seyed Amjad
AU - Ntoutsi, Eirini
N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
PY - 2024/4/25
Y1 - 2024/4/25
N2 - Conventional fair graph clustering methods face two primary challenges: i) They prioritize balanced clusters at the expense of cluster cohesion by imposing rigid constraints, ii) Existing methods of both individual and group-level fairness in graph partitioning mostly rely on eigen decompositions and thus, generally lack interpretability. To address these issues, we propose iFairNMTF, an individual Fairness Nonnegative Matrix Tri-Factorization model with contrastive fairness regularization that achieves balanced and cohesive clusters. By introducing fairness regularization, our model allows for customizable accuracy-fairness trade-offs, thereby enhancing user autonomy without compromising the interpretability provided by nonnegative matrix tri-factorization. Experimental evaluations on real and synthetic datasets demonstrate the superior flexibility of iFairNMTF in achieving fairness and clustering performance.
AB - Conventional fair graph clustering methods face two primary challenges: i) They prioritize balanced clusters at the expense of cluster cohesion by imposing rigid constraints, ii) Existing methods of both individual and group-level fairness in graph partitioning mostly rely on eigen decompositions and thus, generally lack interpretability. To address these issues, we propose iFairNMTF, an individual Fairness Nonnegative Matrix Tri-Factorization model with contrastive fairness regularization that achieves balanced and cohesive clusters. By introducing fairness regularization, our model allows for customizable accuracy-fairness trade-offs, thereby enhancing user autonomy without compromising the interpretability provided by nonnegative matrix tri-factorization. Experimental evaluations on real and synthetic datasets demonstrate the superior flexibility of iFairNMTF in achieving fairness and clustering performance.
KW - Fair Graph Clustering
KW - Fair Unsupervised Learning
KW - Fair-Nonnegative Matrix Factorization
KW - Individual Fairness
UR - http://www.scopus.com/inward/record.url?scp=85192556548&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2402.10756
DO - 10.48550/arXiv.2402.10756
M3 - Conference contribution
AN - SCOPUS:85192556548
SN - 9789819722419
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 284
EP - 296
BT - Advances in Knowledge Discovery and Data Mining
A2 - Yang, De-Nian
A2 - Xie, Xing
A2 - Tseng, Vincent S.
A2 - Pei, Jian
A2 - Huang, Jen-Wei
A2 - Lin, Jerry Chun-Wei
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 7 May 2024 through 10 May 2024
ER -