We investigate graph convolution networks with efficient learning from higher-order graph convolutions and direct learning from adjacency matrices for node classification. We revisit the scaled graph ...

Therefore, another important contribution of this work is to show that while GIN is similar to spectral-domain approaches such as the graph convolutional network (GCN) in learning the spectral filters ...

• We describe the problem of STAGCN by dividing urban areas into grids on the map. Then, we designed a grid embedding network to perform embedding for each grid by graph convolution between the newly ...

The first line of the input contains a positive integer n, the number of vertices in the graph, in the range 1 to 1000. The next lines represents the Adjacency matrix representation of the given graph ...

Abstract. In this paper, we investigate the spectral properties of the adjacency and the Laplacian matrices of random graphs. We prove that: (i) the law of large numbers for the spectral norms and the ...