- Title
- Fairness-aware predictive graph learning in social networks
- Creator
- Wang, Lei; Yu, Shuo; Febrinanto, Falih; Alqahtani, Fayez; El-Tobely, Tarek
- Date
- 2022
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/191879
- Identifier
- vital:17914
- Identifier
-
https://doi.org/10.3390/math10152696
- Identifier
- ISSN:2227-7390 (ISSN)
- Abstract
- Predictive graph learning approaches have been bringing significant advantages in many real-life applications, such as social networks, recommender systems, and other social-related downstream tasks. For those applications, learning models should be able to produce a great prediction result to maximize the usability of their application. However, the paradigm of current graph learning methods generally neglects the differences in link strength, leading to discriminative predictive results, resulting in different performance between tasks. Based on that problem, a fairness-aware predictive learning model is needed to balance the link strength differences and not only consider how to formulate it. To address this problem, we first formally define two biases (i.e., Preference and Favoritism) that widely exist in previous representation learning models. Then, we employ modularity maximization to distinguish strong and weak links from the quantitative perspective. Eventually, we propose a novel predictive learning framework entitled ACE that first implements the link strength differentiated learning process and then integrates it with a dual propagation process. The effectiveness and fairness of our proposed ACE have been verified on four real-world social networks. Compared to nine different state-of-the-art methods, ACE and its variants show better performance. The ACE framework can better reconstruct networks, thus also providing a high possibility of resolving misinformation in graph-structured data. © 2022 by the authors.
- Publisher
- MDPI
- Relation
- Mathematics Vol. 10, no. 15 (2022), p.
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- https://creativecommons.org/licenses/by/4.0/
- Rights
- Copyright © 2022 by the authors
- Rights
- Open Access
- Subject
- 49 Mathematical Sciences; Fairness; Graph Learning; Link Strength; Predictive Learning; Social Networks
- Full Text
- Reviewed
- Funder
- This work was funded by the Researchers Supporting Project No. (RSP2022R509) King Saud University, Riyadh, Saudi Arabia. This work is partially supported by National Natural Science Foundation of China under Grant No. 62102060 and the Fundamental Research Funds for the Central Universities under Grant No. DUT22RC(3)060.
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