Knowledge graph embedding (KGE) is an efficient method to predict missing links in knowledge graphs. Most KGE models based on convolutional neural networks have been designed for improving the ability ...

Graph data is omnipresent and has a wide variety of applications, such as in natural science, social networks, or the semantic web. However, while being rich in information, graphs are often noisy and ...

Citation: Yao R, Shen Z, Xu X, Ling G, Xiang R, Song T, Zhai F and Zhai Y (2024) Knowledge mapping of graph neural networks for drug discovery: a bibliometric and visualized analysis. Front. Pharmacol ...

Node Co-occurrence based Graph Neural Networks for Knowledge Graph Link Prediction This program provides the implementation of our NoGE as described in our paper . Given a knowledge graph, NoGE ...

Revolutionize graph machine learning with large language models (LLMs)! Uncover groundbreaking strategies integrating the might of LLMs like ChatGPT with graph neural networks (GNNs) for unmatched ...

Enhancing Graph Neural Network Performance through Adaptive Knowledge Distillation and Weight Augmentation to optimize their classification and link prediction capabilities. The models are already ...

This framework maps neural network performance to the characteristics of a line graph governed by stochastic gradient descent’s edge dynamics through differential equations. It introduces a neural ...