May 6, 2022 · In this paper, we propose a bi-level optimization approach for learning the optimal graph structure via directly learning the Personalized PageRank propagation ...

Oct 1, 2023 · This paper studies the problem of learning message propagation strategies for graph neural networks (GNNs).

In this paper, we propose a bi-level optimization-based approach for learning the optimal graph structure via directly learning the Personalized PageRank ...

This paper studies the problem of learning message propagation strategies for graph neural networks (GNNs).

ABSTRACT. This paper studies the problem of learning message propagation strategies for graph neural networks (GNNs). One of the challenges.

Oct 1, 2023 · We introduce the optimal propagation steps as latent variables to help find the maximum-likelihood estimation of the GNN parameters in a ...

We propose a data-driven propagation mechanism to adaptively propagate information between layers. Specifically, we construct a bi-level optimization objective.

The de-facto standard algorithm for training Graph Neural Networks (GNNs) is Backpropagation. Despite several advantages, the need to backpropagate gradients ...

Nov 2, 2020 · We can exceed or match the performance of state-of-the-art GNNs by combining shallow models that ignore the graph structure with two simple post-processing ...

This paper studies the problem of learning message propagation strategies for graph neural networks ... optimal propagation steps as latent variables to help find ...