  Graph autoencoders (GAE) and variational graph autoencoders (VGAE) emerged as
two powerful groups of unsupervised node embedding methods, with various
applications to graph-based machine learning problems such as link prediction
and community detection. Nonetheless, at the beginning of this Ph.D. project,
GAE and VGAE models were also suffering from key limitations, preventing them
from being adopted in the industry. In this thesis, we present several
contributions to improve these models, with the general aim of facilitating
their use to address industrial-level problems involving graph representations.
Firstly, we propose two strategies to overcome the scalability issues of
previous GAE and VGAE models, permitting to effectively train these models on
large graphs with millions of nodes and edges. These strategies leverage graph
degeneracy and stochastic subgraph decoding techniques, respectively. Besides,
we introduce Gravity-Inspired GAE and VGAE, providing the first extensions of
these models for directed graphs, that are ubiquitous in industrial
applications. We also consider extensions of GAE and VGAE models for dynamic
graphs. Furthermore, we argue that GAE and VGAE models are often unnecessarily
complex, and we propose to simplify them by leveraging linear encoders. Lastly,
we introduce Modularity-Aware GAE and VGAE to improve community detection on
graphs, while jointly preserving good performances on link prediction. In the
last part of this thesis, we evaluate our methods on several graphs extracted
from the music streaming service Deezer. We put the emphasis on graph-based
music recommendation problems. In particular, we show that our methods can
improve the detection of communities of similar musical items to recommend to
users, that they can effectively rank similar artists in a cold start setting,
and that they permit modeling the music genre perception across cultures.
