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Prerequisite-driven Fair Clustering on Heterogeneous Information Networks

journal contribution
posted on 2023-06-29, 06:59 authored by Juntao Zhang, Sheng Wang, Yuan SunYuan Sun, Zhiyong Peng

This paper studies the problem of fair clustering on heterogeneous information networks (HINs) by considering constraints on structural and sensitive attributes. We propose a Prerequisite-driven Fair Clustering (PDFC ) algorithm to solve this problem. Specifically, we define the structural constraint on the connection among nodes in HINs by combining meta-paths and prerequisite meta-paths and introduce Fairlets as the balance constraint. Under two constraints, we learn node embeddings based on graph models and perform theCholesky decomposition to obtain their orthogonal embeddings. We fuse node embeddings under constraints, define the loss function of PDFC, and perform k-means to achieve clustering. In addition, we design an update strategy of the adjacency matrix to achieve dynamic PDFC over time. Compared with several fair clustering algorithms on three real-world datasets, our experimental results verify the effectiveness and efficiency of PDFC.

Funding

This work was supported by the National Natural Science Foundation of China (No. U1811263 and No. 62202338).

History

Publication Date

2023-06-20

Journal

Proceedings of the ACM on Management of Data

Volume

1

Issue

2

Article Number

122

Pagination

27p. (p. 1-27)

Publisher

Association for Computing Machinery (ACM)

Rights Statement

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. © 2023 Copyright held by the owner/ author(s). Publication rights licensed to ACM.

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