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Measuring and Improving the Use of Graph Information in Graph Neural Networks

arXiv 2206.13170 ↗First posted 27 Jun 2022cs.LG cs.AIcited by 0

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Abstract (arXiv)

Graph neural networks (GNNs) have been widely used for representation learning on graph data. However, there is limited understanding on how much performance GNNs actually gain from graph data. This paper introduces a context-surrounding GNN framework and proposes two smoothness metrics to measure the quantity and quality of information obtained from graph data. A new GNN model, called CS-GNN, is then designed to improve the use of graph information based on the smoothness values of a graph. CS-GNN is shown to achieve better performance than existing methods in different types of real graphs.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 20 of its 34 references that are in the map. First posted on arXiv 27 Jun 2022.

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20 of 34 references.

References · 20

  1. 2017Attention Is All You Needcited by 60,361
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8 more
  1. 2017Graph Signal Processing: Overview, Challenges and Applicationscited by 413
  2. 2018Graph Convolutional Networks for Text Classificationcited by 345
  3. 2017Few-Shot Learning with Graph Neural Networkscited by 324
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  7. 2018Deep Reasoning with Knowledge Graph for Social Relationship Understandingcited by 44
  8. 2019Utilizing Edge Features in Graph Neural Networks via Variational Information Maximizationcited by 10

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