关键词: 关键词: 链接预测, 公平性, 过滤气泡, 预处理, 对抗学习

Abstract: Abstract:Link prediction is a crucial task in network analysis that explores interactions between entities and forecasts potential new relationships in evolving networks. Link prediction may exhibit biases, especially concerning links between entities with sensitive attributes. For example,the issue of "filter bubbles", which amplify the isolation of publicly accessible information online and reduce diversity. To address these challenges, The ALFLP (Adding Link and Adversarial Learning for Fair Link Prediction) method was proposed, which combines preprocessing techniques to balance link density among disadvantaged link groups and adversarial learning methods during processing. The adversarial learning approach involves a generator-discriminator interplay to facilitate more inter-group links, thereby alleviating filter bubble effects. The experimental results on real datasets (pokec_n, pokec_z) show that, compared with baseline methods such as Jaccard, the AUC index of the ALFLP method is improved by about 12 percentage points and 10 percentage points respectively on pokec_n and pokec_z, and the modred index is improved by about 0.14 and 0.1 respectively. The ALFLP method can achieve a better balance between fairness and prediction accuracy.

Key words: Keywords: link prediction, fairness, filter bubbles, preprocessing, adversarial learning