The experimental results show that the proposed method delivers a decent 72. 7% average precision on our dataset.
Graph neural networks (GNNs) have been popularly used in analyzing graph-structured data, showing promising results in various applications such as node classification, link prediction and network recommendation.
Ranked #5 on Node Property Prediction on ogbn-proteins
In this paper, we present a new distributed graph learning system GraphTheta, which supports multiple training strategies and enables efficient and scalable learning on big graphs.
A new variational mode decomposition (VMD) based deep learning approach is proposed in this paper for time series forecasting problem.