  Link weight prediction is of great practical importance, since real-world
networks are often weighted networks. Previous studies have mainly used shallow
graph features for link weight prediction, which limits the prediction
performance. In this paper, we propose a new link weight prediction algorithm,
namely Line Graph Neural Networks for Link Weight Prediction (LGLWP), which
learns deeper graph features through deep learning. In our algorithm, we first
extract the enclosing subgraph around a target link, and then employ a weighted
graph labeling algorithm to label the subgraph nodes. Next, we transform the
subgraph into a line graph and apply the graph convolution neural networks to
learn the node embedding in the line graph, which can represent the links in
the original subgraph. Finally, the link feature vectors are put into a
fully-connected neural network to predict the weight of the target link. Our
algorithm directly obtain the feature vectors of the target links in the
original graph, which is better than the previous methods that splice the node
feature vectors for link weight prediction. Experiments results on six real
datasets of various network sizes and types show that our algorithm has better
prediction performance than the state-of-art methods, while it has fewer
parameters and high training efficiency.
