Graph neural networks (GNNs) have achieved great success in many scenarios
with graph-structured data. However, in many real applications, there are three
issues when applying GNNs: graphs are unknown, nodes have noisy features, and
graphs contain noisy connections. Aiming at solving these problems, we propose
a new graph neural network named as GL-GNN. Our model includes multiple
sub-modules, each sub-module selects important data features and learn the
corresponding key relation graph of data samples when graphs are unknown.
GL-GNN further obtains the network of graphs by learning the network of
sub-modules. The learned graphs are further fused using an aggregation method
over the network of graphs. Our model solves the first issue by simultaneously
learning multiple relation graphs of data samples as well as a relation network
of graphs, and solves the second and the third issue by selecting important
data features as well as important data sample relations. We compare our method
with 14 baseline methods on seven datasets when the graph is unknown and 11
baseline methods on two datasets when the graph is known. The results show that
our method achieves better accuracies than the baseline methods and is capable
of selecting important features and graph edges from the dataset. Our code will
be publicly available at https://github.com/Looomo/GL-GNN.

本篇论文提出了一种新的图形神经网络 GL-GNN，通过学习关系的图形和选择重要数据特征以解决未知图形、节点具有嘈杂特征和图形含有噪声连接等三个问题，并在多个数据集上与基线方法进行对比， 能够选择重要特征和图形边缘。