Title: Global View For GCN: Why Go Deep When You Can Be Shallow?. Abstract: Existing graph convolutional network (GCN) methods attempt to expand the receptive field of its convolution by either stacking up more convolutional layers or accumulating multi-hop adjacency matrices. Either approach increases computation complexity while providing a limited view of the network topology. We propose to extend k-hop adjacency matrices into one generalized exponential matrix to provide GCNs with a global overview of the network topology. This technique allows the GCNs to learn global topology without going deep and with much fewer parameters than most state-of-the-art GCNs, challenging the common assumption that deep GCNs are empirically better for learning global features. We show a significant improvement in performance in semi-supervised learning when this technique is used for common GCNs while maintaining much shallower network architectures ($\leq4$ layers) than the existing ones.