FCMGCMs
FCMGCMs, or Fully Connected Multi-layer Graph Convolutional Networks, are a type of neural network architecture designed to process graph-structured data. They extend the capabilities of traditional Graph Convolutional Networks (GCNs) by incorporating fully connected layers within the message-passing framework. This allows FCMGCMs to learn more complex relationships between nodes in a graph, going beyond local neighborhood aggregation.
The core idea behind FCMGCMs is to integrate standard feedforward neural network layers, often referred to
These architectures have shown promise in various graph-based machine learning tasks, including node classification, graph classification,