  In this work, we focus on the problem of entity alignment in Knowledge Graphs
(KG) and we report on our experiences when applying a Graph Convolutional
Network (GCN) based model for this task. Variants of GCN are used in multiple
state-of-the-art approaches and therefore it is important to understand the
specifics and limitations of GCN-based models. Despite serious efforts, we were
not able to fully reproduce the results from the original paper and after a
thorough audit of the code provided by authors, we concluded, that their
implementation is different from the architecture described in the paper. In
addition, several tricks are required to make the model work and some of them
are not very intuitive. We provide an extensive ablation study to quantify the
effects these tricks and changes of architecture have on final performance.
Furthermore, we examine current evaluation approaches and systematize available
benchmark datasets. We believe that people interested in KG matching might
profit from our work, as well as novices entering the field
