  Graph convolution networks have recently garnered a lot of attention for
representation learning on non-Euclidean feature spaces. Recent research has
focused on stacking multiple layers like in convolutional neural networks for
the increased expressive power of graph convolution networks. However, simply
stacking multiple graph convolution layers lead to issues like vanishing
gradient, over-fitting and over-smoothing. Such problems are much less when
using shallower networks, even though the shallow networks have lower
expressive power. In this work, we propose a novel Multipath Graph
convolutional neural network that aggregates the output of multiple different
shallow networks. We train and test our model on various benchmarks datasets
for the task of node property prediction. Results show that the proposed method
not only attains increased test accuracy but also requires fewer training
epochs to converge. The full implementation is available at
https://github.com/rangan2510/MultiPathGCN
