Topological based classification using graph convolutional networks

Understand

In colored graphs, node classes are often associated with either their neighbors class or with information not incorporated in the graph associated with each node.

• We here propose that node classes are also associated with topological features of the nodes.
• We use this association to improve Graph machine learning in general and specifically, Graph Convolutional Networks (GCN).
• First, we show that even in the absence of any external information on nodes, a good accuracy can be obtained on the prediction of the node class using either topological features, or using the neighbors class as an input to a GCN.