We have 3 of 6 papers
We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.
Not the right person? Other researchers publish under this name.
Generating graph signals is an important task in machine learning, and incorporating the underlying graph into a generative model enables structurally-informed distribution modeling. However, said graph is often noisy or imperfectly estimated, and structural errors can affect the generative process and the resulting si...
This paper derives explicit Wasserstein stability bounds that quantify the effect of relative graph perturbations on the generated distributions and introduces a principled framework for designing stable graph filters that preserve the smoothing behavior of graph heat diffusion, while boosting structural stability.
This paper analyzes continuous normalized flow models parameterized by GNNs and shows that permutation equivariance is preserved for both the resulting continuous-time ordinary differential equations and their discrete numerical approximations used as graph signal samplers.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
