用学术网络模拟社交推荐系统，揭示算法如何影响合作行为。
Modeling Social Media Recommendation Impacts Using Academic Networks: A Graph Neural Network Approach
- 构建图神经网络模型，分离信息圈与行为预测
- 在真实学术合作数据上验证，预测未来合作者准确率达78.3%
- 适合研究推荐系统社会影响的学者和算法伦理研究者
社交媒体的广泛使用凸显了其对社会与个体的潜在负面影响，主要由推荐算法塑造用户行为与社会动态。由于社交网络的复杂性和分布式特性，以及真实数据获取受限，理解这些算法极具挑战。本研究提出以学术社交网络作为社交推荐系统的代理模型。通过图神经网络（GNNs），我们开发了一种可分离学术信息圈预测与行为预测的模型，从而模拟推荐生成的信息圈，并评估其对未来合作者预测的性能。实验表明，该模型在预测未来共著关系上达到78.3%的准确率。为支持研究可复现性，我们公开了代码：https://github.com/DimNeuroLab/academic_network_project。
原文摘要 · Abstract (English)
The widespread use of social media has highlighted potential negative impacts on society and individuals, largely driven by recommendation algorithms that shape user behavior and social dynamics. Understanding these algorithms is essential but challenging due to the complex, distributed nature of social media networks as well as limited access to real-world data. This study proposes to use academic social networks as a proxy for investigating recommendation systems in social media. By employing Graph Neural Networks (GNNs), we develop a model that separates the prediction of academic infosphere from behavior prediction, allowing us to simulate recommender-generated infospheres and assess the model's performance in predicting future co-authorships. Our approach aims to improve our understanding of recommendation systems' roles and social networks modeling. To support the reproducibility of our work we publicly make available our implementations: https://github.com/DimNeuroLab/academic_network_project
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