… line graph neural networks model for the link prediction task, where the original graph is
transformed into a corresponding line graph … employ graph convolution neural networks to learn …

… Teaching students how to read and interpret graphs is a challenge we continually face …
learning progress of one fifth-grade student—Jelani—with regard to the development of her graph …

… , which graph convolution progress can learn edge embeddings from graphs more effectively.
Then we design a novel cross-scale contrastive learning framework on the line graph and …

… line graph concept in graph theory [38], we perform an equivalent mapping of the edges in Gf
to the nodes in a line graph L(… on the line graph as equivalent nodes and equivalent edges. …

… by taking advantage of machine learning algorithms. In this survey, we … of graph learning.
Special attention is paid to four categories of existing graph learning methods, including graph …

… will assume it to form the line graph explicitly and learn the weights of the edges of the line
graph. We will show the trick of avoiding the explicit formation of the line graph at the end of …

… on graphs, we can also study it from a learning perspective. … detection problems in a supervised
learning setting. We show … operator defined on the line graph of edge adjacencies. Our …

… for some existing graph construction methods and provide a platform to develop new graph
learning models; (4) We discuss the relationship between graph learning and several related …

… a target link and then employ a weighted graph labeling algorithm to label the … line graph
and apply graph convolutional neural networks to learn the node embeddings in the line graph, …

… learning node embeddings have been achieved using graph convolution networks [2, 12, 25,
26]. However, when it comes to learning edge embeddings from graphs, graph … line graph …