Graph autoencoders (GAE) and variational graph autoencoders (VGAE) emerged as powerful methods for link prediction (LP). Their performances are less impressive on community detection (CD), where they are often outperformed by simpler alternatives such as the Louvain method. It is still unclear to what extent one can improve CD 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 LP in a multi-task setting. In this workshop paper, summarizing results from our journal publication (Salha-Galvan et al. 2022), we show that jointly addressing these two tasks with high accuracy is possible. For this purpose, we introduce a community-preserving message passing scheme, doping our GAE and VGAE encoders by considering both the initial graph and Louvain-based prior communities when computing embedding spaces. Inspired by modularity-based clustering, we further propose novel training and optimization strategies specifically designed for joint LP and CD. We demonstrate the empirical effectiveness of our approach, referred to as Modularity-Aware GAE and VGAE, on various real-world graphs.
翻译:图形自动转换器(GAE)和变形图形自动转换器(VGAE)作为强有力的连接预测方法出现。在社区探测(CD)方面,其表现不那么令人印象深刻,因为社区探测(CD)往往比Louvain方法等更简单的替代方法表现得更好。目前还不清楚的是,特别是在没有节点特征的情况下,用GAE和VGAE方法改进CD的程度如何。此外,在计算嵌入空间时,人们能否同时同时在多任务环境中保存LP的良好表现。在本讲习班文件中,我们总结了我们期刊出版物(Salha-Galvan等人,2022年)的成果,我们展示了共同以高精确度处理这两项任务的可能性。为此,我们引入了一种社区保存信息传递计划,在计算嵌入空间时既考虑最初的图形,又考虑以前基于Louvain的社区。在基于模块的集束下,我们进一步提出了专门为联合LP和CD设计的新型培训和优化战略。我们展示了我们的方法的经验性有效性,我们称之为Modality-Ewardality、各种图像。