  This paper explores the recently proposed Graph Convolutional Network
architecture proposed in (Kipf & Welling, 2016) The key points of their work is
summarized and their results are reproduced. Graph regularization and
alternative graph convolution approaches are explored. I find that explicit
graph regularization was correctly rejected by (Kipf & Welling, 2016). I
attempt to improve the performance of GCN by approximating a k-step transition
matrix in place of the normalized graph laplacian, but I fail to find positive
results. Nonetheless, the performance of several configurations of this GCN
variation is shown for the Cora, Citeseer, and Pubmed datasets.
