In this study, we classified different origins of three categories of herbal medicines with different feature extraction methods: manual feature extraction, mathematical transformation, deep learning algorithms.
Then we combine GNNs and our proposed variational graph pooling layers for joint graph representation learning and graph coarsening, after which the graph is progressively coarsened to one node.
To characterize complex relations among variables, a relation embedding module is designed in MTHetGNN, where each variable is regarded as a graph node, and each type of edge represents a specific static or dynamic relationship.
In this paper, a MTS forecasting framework that can capture the long-term trends and short-term fluctuations of time series in parallel is proposed.
Detecting cerebral aneurysms is an important clinical task of brain computed tomography angiography (CTA).
Next, a novel graph neural network with an attention mechanism is designed to map each subgraph into an embedding vector.
The ability to compute similarity scores between graphs based on metrics such as Graph Edit Distance (GED) is important in many real-world applications, such as 3D action recognition and biological molecular identification.
Multivariate time series (MTS) forecasting is an important problem in many fields.