The growing popularity of Graph Representation Learning (GRL) methods has resulted in the development of a large number of models applied to a miscellany of domains. Behind this diversity of domains, there is a strong heterogeneity of graphs, making it difficult to estimate the expected performance of a model on a new graph, especially when the graph has distinctive characteristics that have not been encountered in the benchmark yet. To address this, we have developed an experimental pipeline, to assess the impact of a given property on the models performances. In this paper, we use this pipeline to study the effect of two specificities encountered on banks transactional graphs resulting from the partial view a bank has on all the individuals and transactions carried out on the market. These specific features are graph sparsity and asymmetric node information. This study demonstrates the robustness of GRL methods to these distinctive characteristics. We believe that this work can ease the evaluation of GRL methods to specific characteristics and foster the development of such methods on transactional graphs.

翻译：图表代表学习方法越来越受欢迎,导致开发了大量适用于领域差异的模型。在这种领域多样性背后,图表差异很大,难以估计新图表中模型的预期性能,特别是当图表具有基准中尚未发现的独特特征时。为此,我们开发了一个试验性管道,以评估某一财产对模型性能的影响。在本文中,我们利用这一管道研究银行部分观点对市场上进行的所有个人和交易产生的两个特点对银行交易性图的影响。这些具体特征是图表简单度和不对称节点信息。本研究报告表明,GRL方法对这些独特特征的稳健性。我们认为,这项工作可以方便对GRL方法的具体特征进行评估,并促进在交易性图表上开发这类方法。