This Tensorflow implementation of GCN tries to handle graph-classification tasks in two (similar) ways: "global node" approach - for each graph, a global node is added, only global nodes are ...

Graph Classification: Uses the MUTAG dataset (or any TUDataset-compatible dataset). Node Classification : Uses the CORA dataset (or any Planetoid-compatible dataset). Visualization : Includes ROC ...

Graph Convolutional Network. We can say if a convolutional neural network is directly used with the graph for operating and making predictions we can call it a graph convolutional network (GCN). more ...

Visualization results also indicate that D 2-GCN could display clearer classification boundaries and higher intra-class similarity than the baseline disentangled methods. Future work can combine the ...

Research team led by Chuliang Weng introduces D2-GCN, a groundbreaking disentangled graph convolutional network that dynamically adjusts feature channels for enhanced node representation ...

Recently, many researchers have applied Graph Convolutional Neural Networks (GCN) to multi-label learning tasks by establishing relations among labels. However, these researchers have only used GCN as ...

In this study, we propose a graph-based deep learning approach called GCN-LSTM for the classification of viral sequences in metagenomic data. GCN-LSTM utilizes a graph encoding technique to represent ...