Differentiable Graph Module (DGM) for Graph Convolutional Networks

Anees Kazi, Luca Cosmo, Seyed Ahmad Ahmadi, Nassir Navab, Michael Bronstein

Research output: Contribution to journalArticlepeer-review

Abstract

Graph deep learning has recently emerged as a powerful ML concept allowing to generalize successful deep neural architectures to non-Euclidean structured data. One of the limitations of the majority of current graph neural network architectures is that they are often restricted to the transductive setting and rely on the assumption that the underlying graph is known and fixed. Often, this assumption is not true since the graph may be noisy, or partially and even completely unknown. In such cases, it would be helpful to infer the graph directly from the data, especially in inductive settings where some nodes were not present in the graph at training time. Furthermore, learning a graph may become an end in itself, as the inferred structure may provide complementary insights next to the downstream task. In this paper, we introduce Differentiable Graph Module (DGM), a learnable function that predicts edge probabilities in the graph which are optimal for the downstream task. DGM can be combined with convolutional graph neural network layers and trained in an end-to-end fashion. We provide an extensive evaluation on applications in healthcare, brain imaging, computer graphics, and computer vision showing a significant improvement over baselines both in transductive and inductive settings.

Original languageEnglish
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
DOIs
StateAccepted/In press - 2022
Externally publishedYes

Keywords

  • Computational modeling
  • Computer architecture
  • Convolution
  • Disease prediction
  • Graph convolution
  • Graph learning
  • Pipelines
  • Task analysis
  • Topology
  • Training

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