Graph convolutional neural netwoks (GCNNs) have been emerged to handle graph-structured data in recent years. Most existing GCNNs are either spatial approaches working on neighborhood of each node, or ...

In recent years, graph-based multiview clustering methods have become a research hotspot in the clustering field. However, most existing methods lack consideration of cluster balance in their results.

The Herrick Memorial Library, 101 Willard Memorial Square in Wellington, hosted a paper making event for children as part of ongoing children’s programming. Youngsters who attended at the June ...

A total of 2010 patients underwent randomization; 1010 were assigned to ANH and 1000 to usual care. Among patients with available data, 274 of 1005 (27.3%) in the ANH group and 291 of 997 (29.2% ...

The prediction of the projected density of states (PDOS) in materials has traditionally relied on deep learning models based on graph convolutional networks (GCN) and Graph Attention Networks (GAT).