• Transformer (Attention is All you need)

Here are some state-of-the-art Graph Convolutional Network (GCN) models:

• DGCNN: A dynamic graph convolutional neural network that can handle graph data of varying sizes and shapes.

• GIN: The Graph Isomorphism Network, which uses a learnable, permutation-invariant aggregation function to aggregate information from a node's neighbors.

• SAGPool: A GCN model that uses a self-attention mechanism to perform graph pooling, allowing the model to dynamically adjust the size of its receptive field.

• TopoNet: A GCN model that takes into account the topology of the graph when making predictions, allowing it to handle graphs with arbitrary structure.

• Graph U-Net: A GCN model that extends the U-Net architecture, a well-known convolutional neural network for image segmentation, to handle graph data.

• RGCN: A Relational Graph Convolutional Network that is specifically designed to handle relational data, where nodes are connected by relationships rather than simple edges.

• GWNN: A graph wavelet neural network that uses graph wavelet transforms to extract local graph structures, making it well-suited for problems where graph structures have a large impact on the outcome.

These models represent some of the most advanced GCN models available today, and they have been shown to perform well on a wide range of graph-based prediction tasks. If you are working on a problem that involves graph data, it's definitely worth considering one of these models.