  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.
