This is a TensorFlow implementation of the (Variational) Graph Auto-Encoder model ... end-to-end trainable neural network models for unsupervised learning, clustering and link prediction on graphs.

We improve the graph convolutional network (GCN) by replacing the graph convolution matrix with auto-encoder module. The proposed neural network is trained by a multi-task objective function. Except ...

We introduce Relational Graph Convolutional Networks (R-GCNs) and apply them to two standard ... such as DistMult can be significantly improved through the use of an R-GCN encoder model to accumulate ...

To address the above issues in ABSC, we propose a graph convolutional network (GCN) based on dual contrastive learning and syntax label enhancement (i.e., DCL-GCN). First, a contrastive learning-based ...

This repository implements MAGCN, a novel Graph Convolutional Network model that uses multi-view topologies and an attention mechanism to enhance node classification on graph data. The model achieves ...

As for graph state encoder, we refer to method proposed by Kipf and Welling (2016) and build sub-module based on graph convolutional network. In particular ... Then, we combine diagram and formulas to ...

We present Protein Graph Convolutional Network (PGCN), which develops a physically grounded, structure-based molecular interaction graph representation that describes molecular topology and ...