  The automatic verification of document authorships is important in various
settings. Researchers are for example judged and compared by the amount and
impact of their publications and public figures are confronted by their posts
on social media platforms. Therefore, it is important that authorship
information in frequently used web services and platforms is correct. The
question whether a given document is written by a given author is commonly
referred to as authorship verification (AV). While AV is a widely investigated
problem in general, only few works consider settings where the documents are
short and written in a rather uniform style. This makes most approaches
unpractical for online databases and knowledge graphs in the scholarly domain.
Here, authorships of scientific publications have to be verified, often with
just abstracts and titles available. To this point, we present our novel
approach LG4AV which combines language models and graph neural networks for
authorship verification. By directly feeding the available texts in a
pre-trained transformer architecture, our model does not need any hand-crafted
stylometric features that are not meaningful in scenarios where the writing
style is, at least to some extent, standardized. By the incorporation of a
graph neural network structure, our model can benefit from relations between
authors that are meaningful with respect to the verification process. For
example, scientific authors are more likely to write about topics that are
addressed by their co-authors and twitter users tend to post about the same
subjects as people they follow. We experimentally evaluate our model and study
to which extent the inclusion of co-authorships enhances verification decisions
in bibliometric environments.
