Anisotropic Graph Convolutional Network for Semi-Supervised Learning

Abdessamad Ben Hamza

Abstract:

Graph convolutional networks learn effective node
embeddings that have proven to be useful in achieving highaccuracy prediction results in semi-supervised learning tasks, such
as node classification. However, these networks suffer from the issue
of over-smoothing and shrinking effect of the graph due in large
part to the fact that they diffuse features across the edges of the
graph using a linear Laplacian flow. This limitation is especially
problematic for the task of node classification, where the goal is to
predict the label associated with a graph node. To address this issue,
we propose an anisotropic graph convolutional network for semisupervised node classification by introducing a nonlinear function
that captures informative features from nodes, while preventing
oversmoothing. The proposed framework is largely motivated
by the good performance of anisotropic diffusion in image and
geometry processing, and learns nonlinear representations based
on local graph structure and node features. The effectiveness of our
approach is demonstrated on three citation networks and two image
datasets, achieving better or comparable classification accuracy
results compared to the standard baseline methods.