  The main approaches for node classification in graphs are information
propagation and the association of the class of the node with external
information. State of the art methods merge these approaches through Graph
Convolutional Networks. We here use the association of topological features of
the nodes with their class to predict this class. Moreover, combining
topological information with information propagation improves classification
accuracy on the standard CiteSeer and Cora paper classification task.
Topological features and information propagation produce results almost as good
as text-based classification, without no textual or content information. We
propose to represent the topology and information propagation through a GCN
with the neighboring training node classification as an input and the current
node classification as output. Such a formalism outperforms state of the art
methods.
