论文标题
图表示学习方法是否适合图形稀疏性和不对称节点信息？
Are Graph Representation Learning Methods Robust to Graph Sparsity and Asymmetric Node Information?
论文作者
论文摘要
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.为了解决这个问题，我们已经开发了一条实验管道，以评估给定特性对模型性能的影响。 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.这些特定功能是图形稀疏性和不对称节点信息。这项研究证明了GRL方法对这些独特特征的鲁棒性。 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.
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.
