To maximize the homogeneity of these two distinct graphs, we design an improved semi-supervised contrastive loss. In order to enrich scarce label information, we propose a self-supervised mechanism to ...

This paper is to introduce adjacency graph and its adjacency matrix operation, which are used to represent the topological structure and the evolution process of scaling mechanisms. In this paper, the ...

PyTorch Geometric (PyG) remains one of the most used frameworks for geometric deep learning in 2024. It is a versatile solution that goes above PyTorch and provides the means to create Graph Neural ...

The great potential of graph embedding is the main reason of the flourishing of studies in this area and, in particular, the artificial intelligence learning techniques. In this mini review, we give a ...

Matrix factorization-based embedding: The adjacency matrix A ∈ℝ m×n is factorized by two matrices, namely, U ∈ℝ m×d and V ∈ℝ n×d (to be learnt), where d is the dimension of the embedding space. When ...

Accomplishment: Researchers from CCSD at ORNL have developed a distributed PyTorch implementation of multi-headed graph convolutional neural networks (GCNNs) to produce fast and accurate predictions ...