  Abusive behaviors are common on online social networks. The increasing
frequency of antisocial behaviors forces the hosts of online platforms to find
new solutions to address this problem. Automating the moderation process has
thus received a lot of interest in the past few years. Various methods have
been proposed, most based on the exchanged content, and one relying on the
structure and dynamics of the conversation. It has the advantage of being
languageindependent, however it leverages a hand-crafted set of topological
measures which are computationally expensive and not necessarily suitable to
all situations. In the present paper, we propose to use recent graph embedding
approaches to automatically learn representations of conversational graphs
depicting message exchanges. We compare two categories: node vs. whole-graph
embeddings. We experiment with a total of 8 approaches and apply them to a
dataset of online messages. We also study more precisely which aspects of the
graph structure are leveraged by each approach. Our study shows that the
representation produced by certain embeddings captures the information conveyed
by specific topological measures, but misses out other aspects.
