mlsanityData to 14 Oct 2026

Are Graph Representation Learning Methods Robust to Graph Sparsity and Asymmetric Node Information?

arXiv 2205.09648 ↗First posted 19 May 2022cs.LG cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 18 of its 30 references that are in the map. First posted on arXiv 19 May 2022.

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References · 18

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6 more
  1. 2021Are we really making much progress? Revisiting, benchmarking, and refining heterogeneous graph neural networkscited by 176
  2. 2019Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensicscited by 164
  3. 2019Fake News Detection on Social Media using Geometric Deep Learningcited by 118
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  5. 2019DeepTrax: Embedding Graphs of Financial Transactionscited by 15
  6. 2019Keep It Simple: Graph Autoencoders Without Graph Convolutional Networkscited by 14

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