We are the first to fuse negative samples into the graph convolution, yielding several new GCNs boosted by negative samples to improve the quality of node representations and alleviate the ...

Negative samples are usually used in previous contrastive learning methods, but such methods often have major defects in graph contrastive learning. In the knowledge graph, the potential relationship ...

Issue description RuntimeError: Trying to create tensor with negative dimension -1741885395: [-1741885395] I am running a graph convolution network model using Pytorch and get the problem: ...

In a lecture example, we used the convolution integral approach to study the response of an undamped oscillator excited by the rectangular pulse shown below. Here we will apply the graphical ...

Graph convolutional neural networks (GCNs) have achieved great success in graph representation learning by extracting high-level features from nodes and their topology. Since GCNs generally follow a ...