Graph autoencoders (GAE) and variational graph autoencoders (VGAE) emerged as powerful methods for link prediction. Their performances are less impressive on community detection problems where, according to recent and concurring experimental evaluations, they are often outperformed by simpler alternatives such as the Louvain method. It is currently still unclear to which extent one can improve community detection with GAE and VGAE, especially in the absence of node features. It is moreover uncertain whether one could do so while simultaneously preserving good performances on link prediction. In this paper, we show that jointly addressing these two tasks with high accuracy is possible. For this purpose, we introduce and theoretically study a community-preserving message passing scheme, doping our GAE and VGAE encoders by considering both the initial graph structure and modularity-based prior communities when computing embedding spaces. We also propose novel training and optimization strategies, including the introduction of a modularity-inspired regularizer complementing the existing reconstruction losses for joint link prediction and community detection. We demonstrate the empirical effectiveness of our approach, referred to as Modularity-Aware GAE and VGAE, through in-depth experimental validation on various real-world graphs.
翻译:数字自动转换器(GAE)和变形图形自动转换器(VGAE)作为强有力的联系预测方法出现,在社区探测问题上,其表现不那么令人印象深刻,因为根据最近和一致的实验性评价,社区探测问题往往优于Louvain方法等较简单的替代方法,目前还不清楚在何种程度上可以提高GAE和VGAE社区探测能力,特别是在没有节点特征的情况下;此外,还不确定在同时保存连接预测方面的良好性能的同时,是否能够这样做;在本文件中,我们表明,联合处理这两项任务是可能的;为此目的,我们引入并理论上研究一种社区保存信息传递方案,在计算嵌入空间时利用我们的GAE和VGAE编码器,同时考虑最初的图形结构和基于模块的先前社区;我们还提出新的培训和优化战略,包括采用模块化激励型常规化器来补充现有的重建损失,用于联合连接预测和社区探测。我们展示了我们的方法的经验效力,即作为Modulicality-Aware GAE和VGAEE,通过深入的实验性地算。