Title: Is Graph Mixup Beneficial? Investigating Interpolation And Empirical Performance of Graph Mixup Methods
Primary area: learning on graphs and other geometries & topologies
Keywords: Graph Neural Networks, Data Augmentation, Mixup, Graph Classification, Graph Edit Distance, Representation Learning, Evaluation

Abstract: Mixup is a widely used data augmentation technique that constructs new training
  examples by interpolating between existing ones. While effective in domains like
  vision and language, applying mixup to graph data is challenging. In this paper, we
  analyze and empirically explore state-of-the-art graph mixup methods. We conducted an
  independent evaluation following established evaluation protocols for graph
  classification and found that none of the mixup methods yielded statistically
  significant improvements over the no-mixup baseline. To obtain further insights, we
  analyzed the graphs generated from existing mixup methods from an interpolation
  perspective using the graph edit distance. We found that (i) many mixup methods failed
  to interpolate well, (ii) that mixup methods that interpolated well often outperform
  methods that did not, (iii) even optimal interpolation did not lead to performance
  improvements. Our findings highlight the need for a more rigorous exploration and
  evaluation of mixup for graphs.

TL;DR: We investigate graph mixup and analyze prediction performance as well as interpolation properties of prior methods empirically.