2 research outputs found
Topological based classification of paper domains using graph convolutional networks
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
Topological based classification using graph convolutional networks
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.
This accuracy is slightly less than the one that can be obtained using content
based GCN.
Secondly, we show that explicitly adding the topology as an input to the GCN
does not improve the accuracy when combined with external information on nodes.
However, adding an additional adjacency matrix with edges between distant nodes
with similar topology to the GCN does significantly improve its accuracy,
leading to results better than all state of the art methods in multiple
datasets.Comment: arXiv admin note: text overlap with arXiv:1904.0778