Fairness-Aware Predictive Graph Learning in Social Networks

Lei Wang, Shuo Yu, Falih Gozi Febrinanto, Fayez Alqahtani and Tarek E. El-Tobely
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Lei Wang: School of Software, Dalian University of Technology, Dalian 116620, China
Shuo Yu: School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China
Falih Gozi Febrinanto: Institute of Innovation, Science and Sustainability, Federation University Australia, Ballarat, VIC 3353, Australia
Fayez Alqahtani: Software Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh 12372, Saudi Arabia
Tarek E. El-Tobely: Computers and Control Department, Tanta University, Tanta 31527, Egypt

Mathematics, 2022, vol. 10, issue 15, 1-19

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.

Keywords: graph learning; predictive learning; fairness; link strength; social networks (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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