Social networks exhibit a complex graph-like structure due to the uncertainty surrounding potential collaborations among participants. Machine learning algorithms possess generic outstanding performance in multiple real-world prediction tasks. However, whether machine learning algorithms outperform specific algorithms designed for graph link prediction remains unknown to us. To address this issue, the Adamic-Adar Index (AAI), Jaccard Coefficient (JC) and common neighbour centrality (CNC) as representatives of graph-specific algorithms were applied to predict potential collaborations, utilizing data from volunteer activities during the Covid-19 pandemic in Shenzhen city, along with the classical machine learning algorithms such as random forest, support vector machine, and gradient boosting as single predictors and components of ensemble learning. This paper introduces that the AAI algorithm outperformed the traditional JC and CNC, and other machine learning algorithms in analyzing graph node attributes for this task.

翻译：社交网络因参与者之间潜在合作的不确定性而呈现出复杂的图状结构。机器学习算法在多个现实世界的预测任务中展现出卓越的通用性能。然而，机器学习算法是否优于专为图链接预测设计的特定算法仍未知晓。为解决此问题，本文以Adamic-Adar指数（AAI）、Jaccard系数（JC）和共同邻居中心性（CNC）作为图特定算法的代表，利用深圳市新冠疫情期间志愿者活动的数据预测潜在合作，同时采用随机森林、支持向量机和梯度提升等经典机器学习算法作为单一预测器及集成学习的组成部分。本文表明，AAI算法在分析该任务的图节点属性方面优于传统的JC、CNC及其他机器学习算法。