Generative Graph Dictionary Learning

Zhichen Zeng, Ruike Zhu, Yinglong Xia, Hanqing Zeng, Hanghang Tong

Research output: Contribution to journalConference articlepeer-review


Dictionary learning, which approximates data samples by a set of shared atoms, is a fundamental task in representation learning. However, dictionary learning over graphs, namely graph dictionary learning (GDL), is much more challenging than vectorial data as graphs lie in disparate metric spaces. The sparse literature on GDL formulates the problem from the reconstructive view and often learns linear graph embeddings with a high computational cost. In this paper, we propose a Fused Gromov-Wasserstein (FGW) Mixture Model named FRAME to address the GDL problem from the generative view. Equipped with the graph generation function based on the radial basis function kernel and FGW distance, FRAME generates nonlinear embedding spaces, which, as we theoretically proved, provide a good approximation of the original graph spaces. A fast solution is further proposed on top of the expectation-maximization algorithm with guaranteed convergence. Extensive experiments demonstrate the effectiveness of the obtained node and graph embeddings, and our algorithm achieves significant improvements over the state-of-the-art methods.

Original languageEnglish (US)
Pages (from-to)40749-40769
Number of pages21
JournalProceedings of Machine Learning Research
StatePublished - 2023
Event40th International Conference on Machine Learning, ICML 2023 - Honolulu, United States
Duration: Jul 23 2023Jul 29 2023

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering
  • Statistics and Probability


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